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href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +</div> + +<div class="tags"> + +</div> + + + + </div> + + </div> + </div> + </section> + + <footer class="footer"> + <div class="container"> + + + </div> +</footer> + + + <script src="/assets/js/app.js" type="text/javascript"></script><!-- footer scripts --> +<div style="background-color:#A41034"> + <div class="content is-normal has-text-centered"> + <p style="color:white;padding-top:20px;padding-bottom:20px;"><a href="https://scholar.harvard.edu/marinka" style="color:white"><b>Zitnik Lab</b></a> + · <a href="#" style="color:white"><b>Artificial Intelligence in Medicine and Science</b></a> + · <a href="https://harvard.edu" style="color:white"><b>Harvard</b></a> + · <a href="https://dbmi.hms.harvard.edu/" style="color:white"><b>Department of Biomedical Informatics</b></a></p> + </div> +</div></body> +</html> + diff --git a/DMAI/index.html b/DMAI/index.html index d6772830..b27a06ef 100644 --- a/DMAI/index.html +++ b/DMAI/index.html @@ -202,6 +202,90 @@ <h2 id="coordinator">Coordinator</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -290,8 +374,8 @@ <h2 id="coordinator">Coordinator</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -678,90 +762,6 @@ <h2 id="coordinator">Coordinator</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/data/index.html b/data/index.html index 47cfaddb..d5c159c4 100644 --- a/data/index.html +++ b/data/index.html @@ -146,6 +146,41 @@ </div> + <section class="showcase"> +<!-- <figure class="image is-16by9 ">--> +<!-- <img src="" />--> +<!-- </figure>--> + <div class="showcase-content"> + <div class="columns is-centered"> + <div class="column is-8-desktop is-12-tablet"> + <p class="title">ProCyon-Instruct</p> + <p class="subtitle">Foundation Model for Protein Phenotypes</p> + + + + <div class="content"> + <p><p>ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes across five interrelated knowledge domains: molecular functions, therapeutic mechanisms, disease associations, functional protein domains, and molecular interactions. To train ProCyon, we created ProCyon-Instruct, a dataset of 33 million protein phenotype instructions, representing a comprehensive resource for multiscale protein phenotypes.</p> +</p> + </div> + + + + + + + + <a href="https://zitniklab.hms.harvard.edu/ProCyon" class="button is-primary"> + View ProCyon Website + </a> + + + + </div> + </div> + + </div> + </section> + <section class="showcase"> <!-- <figure class="image is-16by9 ">--> <!-- <img src="" />--> @@ -780,6 +815,90 @@ <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -868,8 +987,8 @@ <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -1256,90 +1375,6 @@ </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/feed.xml b/feed.xml index e7c61d9f..6c458642 100644 --- a/feed.xml +++ b/feed.xml @@ -1 +1 @@ -<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.8.6">Jekyll</generator><link href="https://zitniklab.hms.harvard.edu/feed.xml" rel="self" type="application/atom+xml" /><link href="https://zitniklab.hms.harvard.edu/" rel="alternate" type="text/html" /><updated>2024-12-02T21:40:57-05:00</updated><id>https://zitniklab.hms.harvard.edu/feed.xml</id><title type="html">Zitnik Lab</title><subtitle>Harvard Machine Learning for Medicine and Science</subtitle><author><name>Marinka Zitnik</name></author><entry><title type="html">Ayush Noori Selected as a Rhodes Scholar</title><link href="https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar/" rel="alternate" type="text/html" title="Ayush Noori Selected as a Rhodes Scholar" /><published>2024-11-17T00:00:00-05:00</published><updated>2024-11-17T00:00:00-05:00</updated><id>https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar/"><p>Congratulations to <a href="https://www.thecrimson.com/article/2024/11/18/rhodes-scholars-announced-harvard-students/">Ayush Noori on being named a Rhodes Scholar</a>! Such an incredible achievement!</p></content><author><name>Marinka Zitnik</name></author><summary type="html">Congratulations to Ayush Noori on being named a Rhodes Scholar! Such an incredible achievement!</summary></entry><entry><title type="html">PocketGen in Nature Machine Intelligence</title><link href="https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen/" rel="alternate" type="text/html" title="PocketGen in Nature Machine Intelligence" /><published>2024-11-15T00:00:00-05:00</published><updated>2024-11-15T00:00:00-05:00</updated><id>https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen/"><p>PocketGen is a <a href="https://www.nature.com/articles/s42256-024-00920-9">multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">PocketGen is a multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</summary></entry><entry><title type="html">Biomedical AI Agents in Cell</title><link href="https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist/" rel="alternate" type="text/html" title="Biomedical AI Agents in Cell" /><published>2024-11-01T00:00:00-04:00</published><updated>2024-11-01T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist/"><p>We envision “AI scientists” as <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5">AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">We envision “AI scientists” as AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</summary></entry><entry><title type="html">Activity Cliffs in Molecular Property Prediction</title><link href="https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet/" rel="alternate" type="text/html" title="Activity Cliffs in Molecular Property Prediction" /><published>2024-10-19T00:00:00-04:00</published><updated>2024-10-19T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet/"><p>New paper on <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6470c963be16ad5c57f5526c">activity-cliff informed contrastive learning for molecular property prediction.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">New paper on activity-cliff informed contrastive learning for molecular property prediction.</summary></entry><entry><title type="html">Knowledge Graph Agent for Medical Reasoning</title><link href="https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion/" rel="alternate" type="text/html" title="Knowledge Graph Agent for Medical Reasoning" /><published>2024-10-09T00:00:00-04:00</published><updated>2024-10-09T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion/"><p>New paper introducing a <a href="https://arxiv.org/abs/2410.04660">knowledge graph agent for complex, knowledge-intensive medical reasoning.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">New paper introducing a knowledge graph agent for complex, knowledge-intensive medical reasoning.</summary></entry><entry><title type="html">Three Papers Accepted to NeurIPS</title><link href="https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers/" rel="alternate" type="text/html" title="Three Papers Accepted to NeurIPS" /><published>2024-09-27T00:00:00-04:00</published><updated>2024-09-27T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers/"><p>Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</p></content><author><name>Marinka Zitnik</name></author><summary type="html">Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</summary></entry><entry><title type="html">TxGNN Published in Nature Medicine</title><link href="https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine/" rel="alternate" type="text/html" title="TxGNN Published in Nature Medicine" /><published>2024-09-25T00:00:00-04:00</published><updated>2024-09-25T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine/"><p>Graph foundation model for drug repurposing published in <a href="https://www.nature.com/articles/s41591-024-03233-x">Nature Medicine</a>. <a href="https://news.harvard.edu/gazette/story/2024/09/using-ai-to-repurpose-existing-drugs-for-treatment-of-rare-diseases/">[Harvard Gazette]</a> <a href="https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases">[Harvard Medicine News]</a> <a href="https://www.forbes.com/sites/greglicholai/2024/09/26/ai-tool-speeds-drug-repurposing-and-its-free/">[Forbes]</a> <a href="https://developer.nvidia.com/blog/ai-uses-zero-shot-learning-to-find-existing-drugs-for-treating-rare-diseases/">[NVIDIA]</a> <a href="https://kempnerinstitute.harvard.edu/news/txgnn-ai-dr-house-for-disease-treatment/">[Kempner Institute]</a> <a href="https://www.thecrimson.com/article/2024/10/9/drug-repurposing-ai-model/">[Harvard Crimson]</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes] [NVIDIA] [Kempner Institute] [Harvard Crimson]</summary></entry><entry><title type="html">Graph AI in Medicine</title><link href="https://zitniklab.hms.harvard.edu/2024/08/28/GraphAI/" rel="alternate" type="text/html" title="Graph AI in Medicine" /><published>2024-08-28T00:00:00-04:00</published><updated>2024-08-28T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/08/28/GraphAI</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/08/28/GraphAI/"><p>Excited to share a new perspective on <a href="https://go.shr.lc/4g0KpLV">Graph Artificial Intelligence in Medicine</a> in Annual Reviews.</p></content><author><name>Marinka Zitnik</name></author><summary type="html">Excited to share a new perspective on Graph Artificial Intelligence in Medicine in Annual Reviews.</summary></entry><entry><title type="html">How Proteins Behave in Context</title><link href="https://zitniklab.hms.harvard.edu/2024/08/15/PINNACLENews/" rel="alternate" type="text/html" title="How Proteins Behave in Context" /><published>2024-08-15T00:00:00-04:00</published><updated>2024-08-15T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/08/15/PINNACLENews</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/08/15/PINNACLENews/"><p><a href="https://hms.harvard.edu/news/new-ai-tool-captures-how-proteins-behave-context">Harvard Medicine News</a> on our new AI tool that captures how proteins behave in context. <a href="https://kempnerinstitute.harvard.edu/research/deeper-learning/context-matters-for-foundation-models-in-biology/">Kempner Institute</a> on how context matters for foundation models in biology.</p></content><author><name>Marinka Zitnik</name></author><summary type="html">Harvard Medicine News on our new AI tool that captures how proteins behave in context. Kempner Institute on how context matters for foundation models in biology.</summary></entry><entry><title type="html">PINNACLE in Nature Methods</title><link href="https://zitniklab.hms.harvard.edu/2024/07/27/PINNACLENatureMethods/" rel="alternate" type="text/html" title="PINNACLE in Nature Methods" /><published>2024-07-27T00:00:00-04:00</published><updated>2024-07-27T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/07/27/PINNACLENatureMethods</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/07/27/PINNACLENatureMethods/"><p>PINNACLE contextual AI model is published in Nature Methods. <a href="https://www.nature.com/articles/s41592-024-02341-3">Paper.</a> <a href="https://www.nature.com/articles/s41592-024-02342-2">Research Briefing.</a> <a href="https://zitniklab.hms.harvard.edu/projects/PINNACLE/">Project website.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">PINNACLE contextual AI model is published in Nature Methods. Paper. Research Briefing. Project website.</summary></entry></feed> \ No newline at end of file +<?xml version="1.0" encoding="utf-8"?><feed xmlns="http://www.w3.org/2005/Atom" ><generator uri="https://jekyllrb.com/" version="3.8.6">Jekyll</generator><link href="https://zitniklab.hms.harvard.edu/feed.xml" rel="self" type="application/atom+xml" /><link href="https://zitniklab.hms.harvard.edu/" rel="alternate" type="text/html" /><updated>2024-12-16T01:28:04-05:00</updated><id>https://zitniklab.hms.harvard.edu/feed.xml</id><title type="html">Zitnik Lab</title><subtitle>Harvard Machine Learning for Medicine and Science</subtitle><author><name>Marinka Zitnik</name></author><entry><title type="html">Foundation Model for Protein Phenotypes</title><link href="https://zitniklab.hms.harvard.edu/2024/12/16/ProCyon/" rel="alternate" type="text/html" title="Foundation Model for Protein Phenotypes" /><published>2024-12-16T00:00:00-05:00</published><updated>2024-12-16T00:00:00-05:00</updated><id>https://zitniklab.hms.harvard.edu/2024/12/16/ProCyon</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/12/16/ProCyon/"><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">New paper: ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes. [Project website] [Code]</summary></entry><entry><title type="html">SPECTRA in Nature Machine Intelligence</title><link href="https://zitniklab.hms.harvard.edu/2024/12/07/SPECTRA/" rel="alternate" type="text/html" title="SPECTRA in Nature Machine Intelligence" /><published>2024-12-07T00:00:00-05:00</published><updated>2024-12-07T00:00:00-05:00</updated><id>https://zitniklab.hms.harvard.edu/2024/12/07/SPECTRA</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/12/07/SPECTRA/"><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p></content><author><name>Marinka Zitnik</name></author><summary type="html">Are biomedical AI models truly as smart as they seem? SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity. SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</summary></entry><entry><title type="html">Unified Clinical Vocabulary Embeddings</title><link href="https://zitniklab.hms.harvard.edu/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" rel="alternate" type="text/html" title="Unified Clinical Vocabulary Embeddings" /><published>2024-12-07T00:00:00-05:00</published><updated>2024-12-07T00:00:00-05:00</updated><id>https://zitniklab.hms.harvard.edu/2024/12/07/UnifiedClinicalVocabularyEmbeddings</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/12/07/UnifiedClinicalVocabularyEmbeddings/"><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p></content><author><name>Marinka Zitnik</name></author><summary type="html">New paper: A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies. (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</summary></entry><entry><title type="html">Ayush Noori Selected as a Rhodes Scholar</title><link href="https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar/" rel="alternate" type="text/html" title="Ayush Noori Selected as a Rhodes Scholar" /><published>2024-11-17T00:00:00-05:00</published><updated>2024-11-17T00:00:00-05:00</updated><id>https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/11/17/RhodesScholar/"><p>Congratulations to <a href="https://www.thecrimson.com/article/2024/11/18/rhodes-scholars-announced-harvard-students/">Ayush Noori on being named a Rhodes Scholar</a>! Such an incredible achievement!</p></content><author><name>Marinka Zitnik</name></author><summary type="html">Congratulations to Ayush Noori on being named a Rhodes Scholar! Such an incredible achievement!</summary></entry><entry><title type="html">PocketGen in Nature Machine Intelligence</title><link href="https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen/" rel="alternate" type="text/html" title="PocketGen in Nature Machine Intelligence" /><published>2024-11-15T00:00:00-05:00</published><updated>2024-11-15T00:00:00-05:00</updated><id>https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/11/15/PocketGen/"><p>PocketGen is a <a href="https://www.nature.com/articles/s42256-024-00920-9">multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">PocketGen is a multimodal sequence-structure generative model for designing full-atom ligand-binding protein pockets.</summary></entry><entry><title type="html">Biomedical AI Agents in Cell</title><link href="https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist/" rel="alternate" type="text/html" title="Biomedical AI Agents in Cell" /><published>2024-11-01T00:00:00-04:00</published><updated>2024-11-01T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/11/01/AIScientist/"><p>We envision “AI scientists” as <a href="https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5">AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">We envision “AI scientists” as AI agents capable of skeptical learning and reasoning that empower biomedical research by integrating ML models and biomedical tools with experimental platforms.</summary></entry><entry><title type="html">Activity Cliffs in Molecular Properties</title><link href="https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet/" rel="alternate" type="text/html" title="Activity Cliffs in Molecular Properties" /><published>2024-10-19T00:00:00-04:00</published><updated>2024-10-19T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/10/19/ACAnet/"><p>New paper on <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6470c963be16ad5c57f5526c">activity-cliff informed contrastive learning for molecular property prediction.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">New paper on activity-cliff informed contrastive learning for molecular property prediction.</summary></entry><entry><title type="html">Knowledge Graph Agent for Medical Reasoning</title><link href="https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion/" rel="alternate" type="text/html" title="Knowledge Graph Agent for Medical Reasoning" /><published>2024-10-09T00:00:00-04:00</published><updated>2024-10-09T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/10/09/KGARevion/"><p>New paper introducing a <a href="https://arxiv.org/abs/2410.04660">knowledge graph agent for complex, knowledge-intensive medical reasoning.</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">New paper introducing a knowledge graph agent for complex, knowledge-intensive medical reasoning.</summary></entry><entry><title type="html">Three Papers Accepted to NeurIPS</title><link href="https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers/" rel="alternate" type="text/html" title="Three Papers Accepted to NeurIPS" /><published>2024-09-27T00:00:00-04:00</published><updated>2024-09-27T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/09/27/NeurIPS2024Papers/"><p>Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</p></content><author><name>Marinka Zitnik</name></author><summary type="html">Exciting projects include a unified multi-task time series model, a flow-matching approach for generating protein pockets using geometric priors, and a tokenization method that produces invariant molecular representations for integration into large language models.</summary></entry><entry><title type="html">TxGNN Published in Nature Medicine</title><link href="https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine/" rel="alternate" type="text/html" title="TxGNN Published in Nature Medicine" /><published>2024-09-25T00:00:00-04:00</published><updated>2024-09-25T00:00:00-04:00</updated><id>https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine</id><content type="html" xml:base="https://zitniklab.hms.harvard.edu/2024/09/25/TxGNNNatureMedicine/"><p>Graph foundation model for drug repurposing published in <a href="https://www.nature.com/articles/s41591-024-03233-x">Nature Medicine</a>. <a href="https://news.harvard.edu/gazette/story/2024/09/using-ai-to-repurpose-existing-drugs-for-treatment-of-rare-diseases/">[Harvard Gazette]</a> <a href="https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases">[Harvard Medicine News]</a> <a href="https://www.forbes.com/sites/greglicholai/2024/09/26/ai-tool-speeds-drug-repurposing-and-its-free/">[Forbes]</a> <a href="https://developer.nvidia.com/blog/ai-uses-zero-shot-learning-to-find-existing-drugs-for-treating-rare-diseases/">[NVIDIA]</a> <a href="https://kempnerinstitute.harvard.edu/news/txgnn-ai-dr-house-for-disease-treatment/">[Kempner Institute]</a> <a href="https://www.thecrimson.com/article/2024/10/9/drug-repurposing-ai-model/">[Harvard Crimson]</a></p></content><author><name>Marinka Zitnik</name></author><summary type="html">Graph foundation model for drug repurposing published in Nature Medicine. [Harvard Gazette] [Harvard Medicine News] [Forbes] [NVIDIA] [Kempner Institute] [Harvard Crimson]</summary></entry></feed> \ No newline at end of file diff --git a/img/SPECTRA-overview.png b/img/SPECTRA-overview.png index f420ed61..1d690f23 100644 Binary files a/img/SPECTRA-overview.png and b/img/SPECTRA-overview.png differ diff --git a/img/aarthi_venkat.png b/img/aarthi_venkat.png new file mode 100644 index 00000000..3c5c5c6a Binary files /dev/null and b/img/aarthi_venkat.png differ diff --git a/img/katya_ivshina.png b/img/katya_ivshina.png new file mode 100644 index 00000000..d312fe78 Binary files /dev/null and b/img/katya_ivshina.png differ diff --git a/img/michael_sun.png b/img/michael_sun.png new file mode 100644 index 00000000..9f0a74f0 Binary files /dev/null and b/img/michael_sun.png differ diff --git a/index.html b/index.html index cf141ab2..e68f94ce 100644 --- a/index.html +++ b/index.html @@ -177,6 +177,90 @@ <h4 class="has-text-white">AI for Science | Therapeutic Science</h4> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -265,8 +349,8 @@ <h4 class="has-text-white">AI for Science | Therapeutic Science</h4> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -653,90 +737,6 @@ <h4 class="has-text-white">AI for Science | Therapeutic Science</h4> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. 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<a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -347,8 +431,8 @@ <h2 id="visitors-interns-and-short-term-students">Visitors, interns, and short-t <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -735,90 +819,6 @@ <h2 id="visitors-interns-and-short-term-students">Visitors, interns, and short-t </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. 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UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/meetings/index.html b/meetings/index.html index 5ce5f251..9524ffc7 100644 --- a/meetings/index.html +++ b/meetings/index.html @@ -594,6 +594,90 @@ <h3 id="biomedical-data-fusion-embc-and-bc2-2015">Biomedical Data Fusion (EMBC a <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -682,8 +766,8 @@ <h3 id="biomedical-data-fusion-embc-and-bc2-2015">Biomedical Data Fusion (EMBC a <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -1070,90 +1154,6 @@ <h3 id="biomedical-data-fusion-embc-and-bc2-2015">Biomedical Data Fusion (EMBC a </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/news/index.html b/news/index.html index d6d67629..03de2079 100644 --- a/news/index.html +++ b/news/index.html @@ -170,6 +170,90 @@ </nav> </div> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -258,8 +342,8 @@ <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -1534,94 +1618,6 @@ </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2023/02/08/RaincoatPreprint/">New Preprint on Distribution Shifts</a>--> - <p class="card-header-title">Feb 2023: <span class="has-text-primary">New Preprint on Distribution Shifts</span></p> -<!-- <p class="card-header-item">Feb 2023</p>--> -<!-- <p class="card-footer-item">Feb 8, 2023</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>New preprint on <a href="https://arxiv.org/abs/2302.03133">domain adaptation for time series under feature and label shifts.</a> <a href="/projects/Raincoat/">Project website.</a></p> - -</p>--> - <p>New preprint on <a href="https://arxiv.org/abs/2302.03133">domain adaptation for time series under feature and label shifts.</a> <a href="/projects/Raincoat/">Project website.</a></p> - - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2023/02/08/RaincoatPreprint/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Feb 8, 2023</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2023/02/01/PrimeKG/">PrimeKG published in Scientific Data</a>--> - <p class="card-header-title">Feb 2023: <span class="has-text-primary">PrimeKG published in Scientific Data</span></p> -<!-- <p class="card-header-item">Feb 2023</p>--> -<!-- <p class="card-footer-item">Feb 1, 2023</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://www.nature.com/articles/s41597-023-01960-3">Our multimodal knowledge graph for precision medicine</a> is published in Scientific Data. <a href="/projects/PrimeKG/">Project website.</a></p> - -</p>--> - <p><a href="https://www.nature.com/articles/s41597-023-01960-3">Our multimodal knowledge graph for precision medicine</a> is published in Scientific Data. <a href="/projects/PrimeKG/">Project website.</a></p> - - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2023/02/01/PrimeKG/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Feb 1, 2023</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2023/01/20/GNNDelete/">GNNDelete published at ICLR 2023</a>--> - <p class="card-header-title">Jan 2023: <span class="has-text-primary">GNNDelete published at ICLR 2023</span></p> -<!-- <p class="card-header-item">Jan 2023</p>--> -<!-- <p class="card-footer-item">Jan 20, 2023</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>New paper on <a href="https://openreview.net/pdf?id=X9yCkmT5Qrl">machine unlearning for graph neural networks</a> accepted at <a href="https://iclr.cc/">ICLR 2023.</a> <a href="/projects/GNNDelete/">Project website.</a></p> -</p>--> - <p>New paper on <a href="https://openreview.net/pdf?id=X9yCkmT5Qrl">machine unlearning for graph neural networks</a> accepted at <a href="https://iclr.cc/">ICLR 2023.</a> <a href="/projects/GNNDelete/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2023/01/20/GNNDelete/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Jan 20, 2023</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <nav class="pagination is-centered"> diff --git a/news/page2/index.html b/news/page2/index.html index 53e953a2..ac2641af 100644 --- a/news/page2/index.html +++ b/news/page2/index.html @@ -171,6 +171,94 @@ </nav> </div> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2023/02/08/RaincoatPreprint/">New Preprint on Distribution Shifts</a>--> + <p class="card-header-title">Feb 2023: <span class="has-text-primary">New Preprint on Distribution Shifts</span></p> +<!-- <p class="card-header-item">Feb 2023</p>--> +<!-- <p class="card-footer-item">Feb 8, 2023</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New preprint on <a href="https://arxiv.org/abs/2302.03133">domain adaptation for time series under feature and label shifts.</a> <a href="/projects/Raincoat/">Project website.</a></p> + +</p>--> + <p>New preprint on <a href="https://arxiv.org/abs/2302.03133">domain adaptation for time series under feature and label shifts.</a> <a href="/projects/Raincoat/">Project website.</a></p> + + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2023/02/08/RaincoatPreprint/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Feb 8, 2023</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2023/02/01/PrimeKG/">PrimeKG published in Scientific Data</a>--> + <p class="card-header-title">Feb 2023: <span class="has-text-primary">PrimeKG published in Scientific Data</span></p> +<!-- <p class="card-header-item">Feb 2023</p>--> +<!-- <p class="card-footer-item">Feb 1, 2023</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p><a href="https://www.nature.com/articles/s41597-023-01960-3">Our multimodal knowledge graph for precision medicine</a> is published in Scientific Data. <a href="/projects/PrimeKG/">Project website.</a></p> + +</p>--> + <p><a href="https://www.nature.com/articles/s41597-023-01960-3">Our multimodal knowledge graph for precision medicine</a> is published in Scientific Data. <a href="/projects/PrimeKG/">Project website.</a></p> + + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2023/02/01/PrimeKG/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Feb 1, 2023</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2023/01/20/GNNDelete/">GNNDelete published at ICLR 2023</a>--> + <p class="card-header-title">Jan 2023: <span class="has-text-primary">GNNDelete published at ICLR 2023</span></p> +<!-- <p class="card-header-item">Jan 2023</p>--> +<!-- <p class="card-footer-item">Jan 20, 2023</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper on <a href="https://openreview.net/pdf?id=X9yCkmT5Qrl">machine unlearning for graph neural networks</a> accepted at <a href="https://iclr.cc/">ICLR 2023.</a> <a href="/projects/GNNDelete/">Project website.</a></p> +</p>--> + <p>New paper on <a href="https://openreview.net/pdf?id=X9yCkmT5Qrl">machine unlearning for graph neural networks</a> accepted at <a href="https://iclr.cc/">ICLR 2023.</a> <a href="/projects/GNNDelete/">Project website.</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2023/01/20/GNNDelete/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Jan 20, 2023</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -1487,90 +1575,6 @@ </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2021/08/03/NeurIPS-AI-Science/">AI for Science at NeurIPS</a>--> - <p class="card-header-title">Aug 2021: <span class="has-text-primary">AI for Science at NeurIPS</span></p> -<!-- <p class="card-header-item">Aug 2021</p>--> -<!-- <p class="card-footer-item">Aug 3, 2021</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We are organizing the <a href="https://ai4sciencecommunity.github.io/">AI for Science</a> workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.</p> -</p>--> - <p>We are organizing the <a href="https://ai4sciencecommunity.github.io/">AI for Science</a> workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2021/08/03/NeurIPS-AI-Science/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Aug 3, 2021</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2021/08/02/WCB-best-poster/">Best Poster Award at ICML Comp Biology</a>--> - <p class="card-header-title">Aug 2021: <span class="has-text-primary">Best Poster Award at ICML Comp Biology</span></p> -<!-- <p class="card-header-item">Aug 2021</p>--> -<!-- <p class="card-footer-item">Aug 2, 2021</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Congratulations to Michelle for winning the Best Poster Award for her work on <a href="https://arxiv.org/abs/2106.02246">deep contextual learners for protein networks</a> at the <a href="https://icml-compbio.github.io/">ICML Workshop on Computational Biology.</a></p> -</p>--> - <p>Congratulations to Michelle for winning the Best Poster Award for her work on <a href="https://arxiv.org/abs/2106.02246">deep contextual learners for protein networks</a> at the <a href="https://icml-compbio.github.io/">ICML Workshop on Computational Biology.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2021/08/02/WCB-best-poster/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Aug 2, 2021</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2021/07/24/IMLH-best-paper/">Best Paper Award at ICML Interpretable ML</a>--> - <p class="card-header-title">Jul 2021: <span class="has-text-primary">Best Paper Award at ICML Interpretable ML</span></p> -<!-- <p class="card-header-item">Jul 2021</p>--> -<!-- <p class="card-footer-item">Jul 24, 2021</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the <a href="https://sites.google.com/view/imlh2021/program?authuser=0">ICML Interpretable ML in Healthcare Workshop</a>. Stay tuned for more news on this evolving project.</p> -</p>--> - <p>Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the <a href="https://sites.google.com/view/imlh2021/program?authuser=0">ICML Interpretable ML in Healthcare Workshop</a>. Stay tuned for more news on this evolving project.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2021/07/24/IMLH-best-paper/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Jul 24, 2021</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <nav class="pagination is-centered"> diff --git a/news/page3/index.html b/news/page3/index.html index edfd131d..74121966 100644 --- a/news/page3/index.html +++ b/news/page3/index.html @@ -170,6 +170,90 @@ </nav> </div> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2021/08/03/NeurIPS-AI-Science/">AI for Science at NeurIPS</a>--> + <p class="card-header-title">Aug 2021: <span class="has-text-primary">AI for Science at NeurIPS</span></p> +<!-- <p class="card-header-item">Aug 2021</p>--> +<!-- <p class="card-footer-item">Aug 3, 2021</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>We are organizing the <a href="https://ai4sciencecommunity.github.io/">AI for Science</a> workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.</p> +</p>--> + <p>We are organizing the <a href="https://ai4sciencecommunity.github.io/">AI for Science</a> workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2021/08/03/NeurIPS-AI-Science/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Aug 3, 2021</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2021/08/02/WCB-best-poster/">Best Poster Award at ICML Comp Biology</a>--> + <p class="card-header-title">Aug 2021: <span class="has-text-primary">Best Poster Award at ICML Comp Biology</span></p> +<!-- <p class="card-header-item">Aug 2021</p>--> +<!-- <p class="card-footer-item">Aug 2, 2021</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Congratulations to Michelle for winning the Best Poster Award for her work on <a href="https://arxiv.org/abs/2106.02246">deep contextual learners for protein networks</a> at the <a href="https://icml-compbio.github.io/">ICML Workshop on Computational Biology.</a></p> +</p>--> + <p>Congratulations to Michelle for winning the Best Poster Award for her work on <a href="https://arxiv.org/abs/2106.02246">deep contextual learners for protein networks</a> at the <a href="https://icml-compbio.github.io/">ICML Workshop on Computational Biology.</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2021/08/02/WCB-best-poster/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Aug 2, 2021</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2021/07/24/IMLH-best-paper/">Best Paper Award at ICML Interpretable ML</a>--> + <p class="card-header-title">Jul 2021: <span class="has-text-primary">Best Paper Award at ICML Interpretable ML</span></p> +<!-- <p class="card-header-item">Jul 2021</p>--> +<!-- <p class="card-footer-item">Jul 24, 2021</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the <a href="https://sites.google.com/view/imlh2021/program?authuser=0">ICML Interpretable ML in Healthcare Workshop</a>. Stay tuned for more news on this evolving project.</p> +</p>--> + <p>Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the <a href="https://sites.google.com/view/imlh2021/program?authuser=0">ICML Interpretable ML in Healthcare Workshop</a>. Stay tuned for more news on this evolving project.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2021/07/24/IMLH-best-paper/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Jul 24, 2021</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> diff --git a/people/index.html b/people/index.html index bcf3522e..ee1cf9da 100644 --- a/people/index.html +++ b/people/index.html @@ -208,6 +208,35 @@ + <div class="column is-4-desktop is-6-tablet"> + +<!-- <a href="/products/ada_fang/">--> + + <div class="card"> + + + <div class="card-image"> + <figure class="image is-4by3"> + <img src="/img/ada_fang.png" alt="<a href="#">Ada Fang</a>" /> + </figure> + </div> + + <div class="card-content"> + + <p class="title is-5"><a href="#">Ada Fang</a></p> + <p class="subtitle is-6">PhD Student<br/>Harvard CCB<br/></p> +<!-- <p class="subtitle is-5"></p>--> + <p class="title is-5 has-text-right"></p> + + </div> + </div> + +<!-- </a>--> + + </div> + + + <div class="column is-4-desktop is-6-tablet"> <!-- <a href="/products/robert_calef/">--> @@ -239,21 +268,50 @@ <div class="column is-4-desktop is-6-tablet"> -<!-- <a href="/products/ada_fang/">--> +<!-- <a href="/products/katya_ivshina/">--> <div class="card"> <div class="card-image"> <figure class="image is-4by3"> - <img src="/img/ada_fang.png" alt="<a href="#">Ada Fang</a>" /> + <img src="/img/katya_ivshina.png" alt="<a href="https://katyaivshina.com/">Katya Ivshina</a>" /> </figure> </div> <div class="card-content"> - <p class="title is-5"><a href="#">Ada Fang</a></p> - <p class="subtitle is-6">PhD Student<br/>Harvard CCB<br/></p> + <p class="title is-5"><a href="https://katyaivshina.com/">Katya Ivshina</a></p> + <p class="subtitle is-6">PhD Student<br/>Harvard Applied Mathematics<br/></p> +<!-- <p class="subtitle is-5"></p>--> + <p class="title is-5 has-text-right"></p> + + </div> + </div> + +<!-- </a>--> + + </div> + + + + <div class="column is-4-desktop is-6-tablet"> + +<!-- <a href="/products/michael_sun/">--> + + <div class="card"> + + + <div class="card-image"> + <figure class="image is-4by3"> + <img src="/img/michael_sun.png" alt="<a href="https://michaelsuntech.wordpress.com/">Michael Sun</a>" /> + </figure> + </div> + + <div class="card-content"> + + <p class="title is-5"><a href="https://michaelsuntech.wordpress.com/">Michael Sun</a></p> + <p class="subtitle is-6">PhD Student<br/>MIT EECS<br/></p> <!-- <p class="subtitle is-5"></p>--> <p class="title is-5 has-text-right"></p> @@ -643,6 +701,35 @@ + <div class="column is-4-desktop is-6-tablet"> + +<!-- <a href="/products/aarthi_venkat/">--> + + <div class="card"> + + + <div class="card-image"> + <figure class="image is-4by3"> + <img src="/img/aarthi_venkat.png" alt="<a href="https://scholar.google.com/citations?user=Z8c9_0QAAAAJ&hl=en">Aarthi Venkat</a>" /> + </figure> + </div> + + <div class="card-content"> + + <p class="title is-5"><a href="https://scholar.google.com/citations?user=Z8c9_0QAAAAJ&hl=en">Aarthi Venkat</a></p> + <p class="subtitle is-6">Postdoctoral Fellow<br/>Eric and Wendy Schmidt Fellow<br/></p> +<!-- <p class="subtitle is-5"></p>--> + <p class="title is-5 has-text-right"></p> + + </div> + </div> + +<!-- </a>--> + + </div> + + + <div class="column is-4-desktop is-6-tablet"> <!-- <a href="/products/kexin_chen/">--> @@ -674,20 +761,20 @@ <div class="column is-4-desktop is-6-tablet"> -<!-- <a href="/products/pengwei_sui/">--> +<!-- <a href="/products/michelle_dai/">--> <div class="card"> <div class="card-image"> <figure class="image is-4by3"> - <img src="/img/pengwei_sui.png" alt="<a href="">Pengwei Sui</a>" /> + <img src="/img/michelle_dai.png" alt="<a href="#">Michelle Dai</a>" /> </figure> </div> <div class="card-content"> - <p class="title is-5"><a href="">Pengwei Sui</a></p> + <p class="title is-5"><a href="#">Michelle Dai</a></p> <p class="subtitle is-6">Research Associate<br/><br/></p> <!-- <p class="subtitle is-5"></p>--> <p class="title is-5 has-text-right"></p> @@ -703,20 +790,20 @@ <div class="column is-4-desktop is-6-tablet"> -<!-- <a href="/products/michelle_dai/">--> +<!-- <a href="/products/pengwei_sui/">--> <div class="card"> <div class="card-image"> <figure class="image is-4by3"> - <img src="/img/michelle_dai.png" alt="<a href="#">Michelle Dai</a>" /> + <img src="/img/pengwei_sui.png" alt="<a href="">Pengwei Sui</a>" /> </figure> </div> <div class="card-content"> - <p class="title is-5"><a href="#">Michelle Dai</a></p> + <p class="title is-5"><a href="">Pengwei Sui</a></p> <p class="subtitle is-6">Research Associate<br/><br/></p> <!-- <p class="subtitle is-5"></p>--> <p class="title is-5 has-text-right"></p> @@ -790,20 +877,20 @@ <div class="column is-4-desktop is-6-tablet"> -<!-- <a href="/products/richard_zhu/">--> +<!-- <a href="/products/inaki_arango/">--> <div class="card"> <div class="card-image"> <figure class="image is-4by3"> - <img src="/img/richard_zhu.png" alt="<a href="#">Richard Zhu</a>" /> + <img src="/img/inaki_arango.png" alt="<a href="#">Iñaki Arango</a>" /> </figure> </div> <div class="card-content"> - <p class="title is-5"><a href="#">Richard Zhu</a></p> + <p class="title is-5"><a href="#">Iñaki Arango</a></p> <p class="subtitle is-6">Undergraduate Researcher<br/>Harvard<br/></p> <!-- <p class="subtitle is-5"></p>--> <p class="title is-5 has-text-right"></p> @@ -819,20 +906,20 @@ <div class="column is-4-desktop is-6-tablet"> -<!-- <a href="/products/ayush_noori/">--> +<!-- <a href="/products/richard_zhu/">--> <div class="card"> <div class="card-image"> <figure class="image is-4by3"> - <img src="/img/ayush_noori.png" alt="<a href="https://www.ayushnoori.com/">Ayush Noori</a>" /> + <img src="/img/richard_zhu.png" alt="<a href="#">Richard Zhu</a>" /> </figure> </div> <div class="card-content"> - <p class="title is-5"><a href="https://www.ayushnoori.com/">Ayush Noori</a></p> + <p class="title is-5"><a href="#">Richard Zhu</a></p> <p class="subtitle is-6">Undergraduate Researcher<br/>Harvard<br/></p> <!-- <p class="subtitle is-5"></p>--> <p class="title is-5 has-text-right"></p> @@ -848,20 +935,20 @@ <div class="column is-4-desktop is-6-tablet"> -<!-- <a href="/products/inaki_arango/">--> +<!-- <a href="/products/ayush_noori/">--> <div class="card"> <div class="card-image"> <figure class="image is-4by3"> - <img src="/img/inaki_arango.png" alt="<a href="#">Iñaki Arango</a>" /> + <img src="/img/ayush_noori.png" alt="<a href="https://www.ayushnoori.com/">Ayush Noori</a>" /> </figure> </div> <div class="card-content"> - <p class="title is-5"><a href="#">Iñaki Arango</a></p> + <p class="title is-5"><a href="https://www.ayushnoori.com/">Ayush Noori</a></p> <p class="subtitle is-6">Undergraduate Researcher<br/>Harvard<br/></p> <!-- <p class="subtitle is-5"></p>--> <p class="title is-5 has-text-right"></p> @@ -936,6 +1023,12 @@ <h2 id="Associate members">Associate members</h2> + + + + + + @@ -1014,6 +1107,90 @@ <h2 id="Alumni">Lab alumni</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -1102,8 +1279,8 @@ <h2 id="Alumni">Lab alumni</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -1490,90 +1667,6 @@ <h2 id="Alumni">Lab alumni</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/postdoc-ML/index.html b/postdoc-ML/index.html index 9855a701..df1f6aea 100644 --- a/postdoc-ML/index.html +++ b/postdoc-ML/index.html @@ -191,6 +191,90 @@ <h2 id="advisor">Advisor</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -279,8 +363,8 @@ <h2 id="advisor">Advisor</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -667,90 +751,6 @@ <h2 id="advisor">Advisor</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/postdoc-TDC/index.html b/postdoc-TDC/index.html index 24028880..fcebe73f 100644 --- a/postdoc-TDC/index.html +++ b/postdoc-TDC/index.html @@ -203,6 +203,90 @@ <h2 id="advisor">Advisor</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -291,8 +375,8 @@ <h2 id="advisor">Advisor</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -679,90 +763,6 @@ <h2 id="advisor">Advisor</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/postdoc-biomedicalAI-MGB/index.html b/postdoc-biomedicalAI-MGB/index.html index 1816abb4..7495b91d 100644 --- a/postdoc-biomedicalAI-MGB/index.html +++ b/postdoc-biomedicalAI-MGB/index.html @@ -180,6 +180,90 @@ <h2 id="application-process">Application process</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -268,8 +352,8 @@ <h2 id="application-process">Application process</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -656,90 +740,6 @@ <h2 id="application-process">Application process</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/postdoc-cancerTxAI/index.html b/postdoc-cancerTxAI/index.html index 95eea48c..c10cc582 100644 --- a/postdoc-cancerTxAI/index.html +++ b/postdoc-cancerTxAI/index.html @@ -189,6 +189,90 @@ <h2 id="faculty-and-mentors">Faculty and mentors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -277,8 +361,8 @@ <h2 id="faculty-and-mentors">Faculty and mentors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -665,90 +749,6 @@ <h2 id="faculty-and-mentors">Faculty and mentors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/postdoc-medicalAI/index.html b/postdoc-medicalAI/index.html index 5e5b3c78..3617bf32 100644 --- a/postdoc-medicalAI/index.html +++ b/postdoc-medicalAI/index.html @@ -196,6 +196,90 @@ <h2 id="advisor">Advisor</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -284,8 +368,8 @@ <h2 id="advisor">Advisor</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -672,90 +756,6 @@ <h2 id="advisor">Advisor</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/products/jason_poulos/index.html b/products/aarthi_venkat/index.html similarity index 80% rename from products/jason_poulos/index.html rename to products/aarthi_venkat/index.html index 2d9421e0..f9c81612 100644 --- a/products/jason_poulos/index.html +++ b/products/aarthi_venkat/index.html @@ -4,33 +4,33 @@ <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> - <title><a href="https://jasonvpoulos.com/">Jason Poulos</a> - Zitnik Lab</title> + <title><a href="https://scholar.google.com/citations?user=Z8c9_0QAAAAJ&hl=en">Aarthi Venkat</a> - Zitnik Lab</title> <link rel="stylesheet" href="/assets/css/app.css"> <link rel="shortcut icon" type="image/png" href="/favicon.png" /> <script defer src="https://use.fontawesome.com/releases/v5.3.1/js/all.js"></script> <!-- Begin Jekyll SEO tag v2.6.1 --> -<title>Jason Poulos | Zitnik Lab</title> +<title>Aarthi Venkat | Zitnik Lab</title> <meta name="generator" content="Jekyll v3.8.6" /> -<meta property="og:title" content="Jason Poulos" /> +<meta property="og:title" content="Aarthi Venkat" /> <meta name="author" content="Marinka Zitnik" /> <meta property="og:locale" content="en_US" /> <meta name="description" content="Artificial Intelligence (AI), Medicine, Science, and Drug Discovery" /> <meta property="og:description" content="Artificial Intelligence (AI), Medicine, Science, and Drug Discovery" /> -<link 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content="Jason Poulos" /> +<meta property="twitter:image" content="https://zitniklab.hms.harvard.edu/img/aarthi_venkat.png" /> +<meta property="twitter:title" content="Aarthi Venkat" /> <meta name="twitter:site" content="@marinkazitnik" /> <meta name="twitter:creator" content="@Marinka Zitnik" /> <script type="application/ld+json"> -{"image":"https://zitniklab.hms.harvard.edu/img/jason_poulos.png","url":"https://zitniklab.hms.harvard.edu/products/jason_poulos/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/jason_poulos/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Jason Poulos","dateModified":"2024-11-17T13:35:16-05:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-11-17T13:35:16-05:00","@type":"BlogPosting","@context":"https://schema.org"}</script> +{"image":"https://zitniklab.hms.harvard.edu/img/aarthi_venkat.png","url":"https://zitniklab.hms.harvard.edu/products/aarthi_venkat/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/aarthi_venkat/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Aarthi Venkat","dateModified":"2024-12-16T01:28:04-05:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-12-16T01:28:04-05:00","@type":"BlogPosting","@context":"https://schema.org"}</script> <!-- End Jekyll SEO tag --> <script async src="https://www.googletagmanager.com/gtag/js?id=UA-162129505-1"></script> <script> @@ -125,8 +125,8 @@ <section class="hero is-medium is-bold is-primary" style="background: url('/hero.jpg') no-repeat center center; background-size: cover;" > <div class="hero-body"> <div class="container"> - <p class="title is-2"><a href="https://jasonvpoulos.com/">Jason Poulos</a></p> - <p class="subtitle is-3">Postdoctoral Fellow<br/>Brigham and Women's Hospital<br/></p> + <p class="title is-2"><a href="https://scholar.google.com/citations?user=Z8c9_0QAAAAJ&hl=en">Aarthi Venkat</a></p> + <p class="subtitle is-3">Postdoctoral Fellow<br/>Eric and Wendy Schmidt Fellow<br/></p> </div> </div> @@ -146,13 +146,13 @@ <div class="column is-6"> <figure class="image is-4by3"> - <img src="/img/jason_poulos.png" /> + <img src="/img/aarthi_venkat.png" /> </figure> </div> <div class="column is-6"> - <p class="title is-3"><a href="https://jasonvpoulos.com/">Jason Poulos</a></p> - <p class="subtitle is-3">Postdoctoral Fellow<br/>Brigham and Women's Hospital<br/></p> + <p class="title is-3"><a href="https://scholar.google.com/citations?user=Z8c9_0QAAAAJ&hl=en">Aarthi Venkat</a></p> + <p class="subtitle is-3">Postdoctoral Fellow<br/>Eric and Wendy Schmidt Fellow<br/></p> <p class="title is-4 has-text-right"></p> diff --git a/products/ada_fang/index.html b/products/ada_fang/index.html index 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class="hero is-medium is-bold is-primary" style="background: url('/hero.jpg') no-repeat center center; background-size: cover;" > + <div class="hero-body"> + <div class="container"> + <p class="title is-2"><a href="https://michaelsuntech.wordpress.com/">Michael Sun</a></p> + <p class="subtitle is-3">PhD Student<br/>MIT EECS<br/></p> + + </div> + </div> +</section> + + + + + <section class="section"> + <div class="container"> + <div class="columns"> + + <div class="column is-12"> + + + <div class="columns is-multiline"> + + <div class="column is-6"> + <figure class="image is-4by3"> + <img src="/img/michael_sun.png" /> + </figure> + </div> + + <div class="column is-6"> + <p class="title is-3"><a href="https://michaelsuntech.wordpress.com/">Michael Sun</a></p> + <p class="subtitle is-3">PhD Student<br/>MIT EECS<br/></p> + <p class="title is-4 has-text-right"></p> + + + + + + </div> + + <div class="column is-12"> + <p class="title is-4">Description</p> + <div class="content"> + + + + + 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name="twitter:site" content="@marinkazitnik" /> <meta name="twitter:creator" content="@Marinka Zitnik" /> <script type="application/ld+json"> -{"image":"https://zitniklab.hms.harvard.edu/img/ying_jin.png","url":"https://zitniklab.hms.harvard.edu/products/ying_jin/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/ying_jin/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Ying Jin","dateModified":"2024-12-02T21:40:57-05:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-12-02T21:40:57-05:00","@type":"BlogPosting","@context":"https://schema.org"}</script> +{"image":"https://zitniklab.hms.harvard.edu/img/ying_jin.png","url":"https://zitniklab.hms.harvard.edu/products/ying_jin/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/ying_jin/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Ying 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property="twitter:image" content="https://zitniklab.hms.harvard.edu/img/zhenglun_kong.png" /> <meta property="twitter:title" content="Zhenglun Kong" /> <meta name="twitter:site" content="@marinkazitnik" /> <meta name="twitter:creator" content="@Marinka Zitnik" /> <script type="application/ld+json"> -{"image":"https://zitniklab.hms.harvard.edu/img/zhenglun_kong.png","url":"https://zitniklab.hms.harvard.edu/products/zhenglun_kong/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/zhenglun_kong/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Zhenglun Kong","dateModified":"2024-12-02T21:40:57-05:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-12-02T21:40:57-05:00","@type":"BlogPosting","@context":"https://schema.org"}</script> +{"image":"https://zitniklab.hms.harvard.edu/img/zhenglun_kong.png","url":"https://zitniklab.hms.harvard.edu/products/zhenglun_kong/","mainEntityOfPage":{"@type":"WebPage","@id":"https://zitniklab.hms.harvard.edu/products/zhenglun_kong/"},"author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Zhenglun Kong","dateModified":"2024-12-16T01:28:04-05:00","description":"Artificial Intelligence (AI), Medicine, Science, and Drug Discovery","datePublished":"2024-12-16T01:28:04-05:00","@type":"BlogPosting","@context":"https://schema.org"}</script> <!-- End Jekyll SEO tag --> <script async src="https://www.googletagmanager.com/gtag/js?id=UA-162129505-1"></script> <script> diff --git a/projects/Clinical-knowledge-embeddings/index.html b/projects/Clinical-knowledge-embeddings/index.html index 7d0b7d2b..4361af16 100644 --- a/projects/Clinical-knowledge-embeddings/index.html +++ b/projects/Clinical-knowledge-embeddings/index.html @@ -203,15 +203,15 @@ <h2 id="clinical-vocabulary-embeddings-capture-medical-knowledge-consensus-acros <h2 id="publication">Publication</h2> -<p><a href="#">Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine</a><br /> +<p><a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine</a><br /> Ruth Johnson, Uri Gottlieb, Galit Shaham, Lihi Eisen, Jacob Waxman, Stav Devons-Sberro, Curtis R. Ginder, Peter Hong, Raheel Sayeed, Ben Y. Reis, Ran D. Balicer, Noa Dagan, and Marinka Zitnik<br /> -<em>In Review</em> 2024 <a href="#">[MedRxiv]</a></p> +<em>In Review</em> 2024 <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">[medRxiv]</a></p> <div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code>@article{johnson2024unified, title={Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine}, author={Johnson, Ruth and Gottlieb, Uri and Shaham, Galit and Eisen, Lihi and Waxman, Jacob and Devons-Sberro, Stav and Ginder, Curtis R. and Hong, Peter and Sayeed, Raheel and Reis, Ben Y. and Balicer, Ran D. and Dagan, Noa and Zitnik, Marinka}, journal={medrxiv}, - url={}, + url={https://www.medrxiv.org/content/10.1101/2024.12.03.24318322}, year={2024} } </code></pre></div></div> @@ -252,6 +252,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -340,8 +424,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -728,90 +812,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/G-Meta/index.html b/projects/G-Meta/index.html index 55956c20..24226aaf 100644 --- a/projects/G-Meta/index.html +++ b/projects/G-Meta/index.html @@ -211,6 +211,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -299,8 +383,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -687,90 +771,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/GNNDelete/index.html b/projects/GNNDelete/index.html index 1842bf9b..6f976cb3 100644 --- a/projects/GNNDelete/index.html +++ b/projects/GNNDelete/index.html @@ -226,6 +226,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -314,8 +398,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -702,90 +786,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/GNNGuard/index.html b/projects/GNNGuard/index.html index e660b245..5fe0dcfc 100644 --- a/projects/GNNGuard/index.html +++ b/projects/GNNGuard/index.html @@ -203,6 +203,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -291,8 +375,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -679,90 +763,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/GraphXAI/index.html b/projects/GraphXAI/index.html index d5cf1fca..4f98b565 100644 --- a/projects/GraphXAI/index.html +++ b/projects/GraphXAI/index.html @@ -259,6 +259,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -347,8 +431,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -735,90 +819,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/KGARevion/index.html b/projects/KGARevion/index.html index 3eb8b429..69b85d8e 100644 --- a/projects/KGARevion/index.html +++ b/projects/KGARevion/index.html @@ -209,6 +209,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -297,8 +381,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -685,90 +769,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/Madrigal/index.html b/projects/Madrigal/index.html index 9a39cfba..e05b8ec5 100644 --- a/projects/Madrigal/index.html +++ b/projects/Madrigal/index.html @@ -201,6 +201,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -289,8 +373,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -677,90 +761,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/Milieu/index.html b/projects/Milieu/index.html index 32764091..cd8da151 100644 --- a/projects/Milieu/index.html +++ b/projects/Milieu/index.html @@ -216,6 +216,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -304,8 +388,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -692,90 +776,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/NIFTY/index.html b/projects/NIFTY/index.html index a82da201..5413baca 100644 --- a/projects/NIFTY/index.html +++ b/projects/NIFTY/index.html @@ -236,6 +236,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -324,8 +408,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -712,90 +796,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/PDGrapher/index.html b/projects/PDGrapher/index.html index b7ddced6..81e3ad83 100644 --- a/projects/PDGrapher/index.html +++ b/projects/PDGrapher/index.html @@ -238,6 +238,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -326,8 +410,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -714,90 +798,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/PINNACLE/index.html b/projects/PINNACLE/index.html index ef0d2010..dea899a1 100644 --- a/projects/PINNACLE/index.html +++ b/projects/PINNACLE/index.html @@ -253,6 +253,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -341,8 +425,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -729,90 +813,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/PocketFlow/index.html b/projects/PocketFlow/index.html index f93b98ac..05e1e473 100644 --- a/projects/PocketFlow/index.html +++ b/projects/PocketFlow/index.html @@ -193,6 +193,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -281,8 +365,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -669,90 +753,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/PocketGen/index.html b/projects/PocketGen/index.html index 9f312522..5f9054aa 100644 --- a/projects/PocketGen/index.html +++ b/projects/PocketGen/index.html @@ -233,6 +233,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -321,8 +405,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -709,90 +793,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/PrimeKG/index.html b/projects/PrimeKG/index.html index 212a4a55..59b110c3 100644 --- a/projects/PrimeKG/index.html +++ b/projects/PrimeKG/index.html @@ -209,6 +209,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -297,8 +381,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -685,90 +769,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/REMAP/index.html b/projects/REMAP/index.html index 47b4dea8..68d04a96 100644 --- a/projects/REMAP/index.html +++ b/projects/REMAP/index.html @@ -220,6 +220,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -308,8 +392,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -696,90 +780,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/Raincoat/index.html b/projects/Raincoat/index.html index f40988e2..a7dda5e0 100644 --- a/projects/Raincoat/index.html +++ b/projects/Raincoat/index.html @@ -258,6 +258,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -346,8 +430,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -734,90 +818,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/Raindrop/index.html b/projects/Raindrop/index.html index c7496212..b03e72f1 100644 --- a/projects/Raindrop/index.html +++ b/projects/Raindrop/index.html @@ -234,6 +234,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -322,8 +406,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -710,90 +794,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/SHEPHERD/index.html b/projects/SHEPHERD/index.html index e1bffb1b..da538a1d 100644 --- a/projects/SHEPHERD/index.html +++ b/projects/SHEPHERD/index.html @@ -255,6 +255,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -343,8 +427,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -731,90 +815,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/SIPT/index.html b/projects/SIPT/index.html index d07440c9..dda88a8a 100644 --- a/projects/SIPT/index.html +++ b/projects/SIPT/index.html @@ -217,6 +217,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -305,8 +389,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -693,90 +777,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/SPECTRA/index.html b/projects/SPECTRA/index.html index d2840e3e..ec724a0c 100644 --- a/projects/SPECTRA/index.html +++ b/projects/SPECTRA/index.html @@ -4,16 +4,16 @@ <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1"> - <title>Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets - Zitnik Lab</title> + <title>Evaluating Generalizability of Molecular AI Models - Zitnik Lab</title> <link rel="stylesheet" href="/assets/css/app.css"> <link rel="shortcut icon" type="image/png" href="/favicon.png" /> <script defer src="https://use.fontawesome.com/releases/v5.3.1/js/all.js"></script> <!-- Begin Jekyll SEO tag v2.6.1 --> -<title>Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets | Zitnik Lab</title> +<title>Evaluating Generalizability of Molecular AI Models | Zitnik Lab</title> <meta name="generator" content="Jekyll v3.8.6" /> -<meta property="og:title" content="Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets" /> +<meta property="og:title" content="Evaluating Generalizability of Molecular AI Models" /> <meta name="author" content="Marinka Zitnik" /> <meta property="og:locale" content="en_US" /> <meta name="description" content="SPECTRA paves the way for a more comprehensive evaluation of foundation models in molecular biology." /> @@ -22,11 +22,11 @@ <meta property="og:url" content="https://zitniklab.hms.harvard.edu/projects/SPECTRA/" /> <meta property="og:site_name" content="Zitnik Lab" /> <meta name="twitter:card" content="summary" /> -<meta property="twitter:title" content="Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets" /> +<meta property="twitter:title" content="Evaluating Generalizability of Molecular AI Models" /> <meta name="twitter:site" content="@marinkazitnik" /> <meta name="twitter:creator" content="@Marinka Zitnik" /> <script type="application/ld+json"> -{"url":"https://zitniklab.hms.harvard.edu/projects/SPECTRA/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets","description":"SPECTRA paves the way for a more comprehensive evaluation of foundation models in molecular biology.","@type":"WebPage","@context":"https://schema.org"}</script> +{"url":"https://zitniklab.hms.harvard.edu/projects/SPECTRA/","author":{"@type":"Person","name":"Marinka Zitnik"},"headline":"Evaluating Generalizability of Molecular AI Models","description":"SPECTRA paves the way for a more comprehensive evaluation of foundation models in molecular biology.","@type":"WebPage","@context":"https://schema.org"}</script> <!-- End Jekyll SEO tag --> <script async src="https://www.googletagmanager.com/gtag/js?id=UA-162129505-1"></script> <script> @@ -121,7 +121,7 @@ <section class="hero is-medium is-bold is-primary" style="background: url('/hero.jpg') no-repeat center center; background-size: cover;" > <div class="hero-body"> <div class="container"> - <p class="title is-2">Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets</p> + <p class="title is-2">Evaluating Generalizability of Molecular AI Models</p> <p class="subtitle is-3"></p> </div> @@ -143,23 +143,16 @@ <div class="box has-background-info has-text-white"> <p> -Deep learning has made rapid advances in modeling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. +Deep learning has made rapid advances in modelling molecular sequencing data. Despite achieving high performance on benchmarks, it remains unclear to what extent deep learning models learn general principles and generalize to previously unseen sequences. Benchmarks traditionally interrogate model generalizability by generating metadata- or sequence similarity-based train and test splits of input data before assessing model performance. </p> - -<p> -Benchmarks traditionally interrogate model generalizability by generating metadata based (MB) or sequence-similarity based (SB) train and test splits of input data before assessing model performance. Here, we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, i.e., similarity between train and test splits. -</p> - <p> -We introduce SPECTRA, a spectral framework for comprehensive model evaluation. For a given model and input data, SPECTRA plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. +Here we show that this approach mischaracterizes model generalizability by failing to consider the full spectrum of cross-split overlap, that is, similarity between train and test splits. We introduce SPECTRA, the spectral framework for model evaluation. Given a model and a dataset, SPECTRA plots model performance as a function of decreasing cross-split overlap and reports the area under this curve as a measure of generalizability. </p> - <p> -We use SPECTRA with 18 sequencing datasets and phenotypes ranging from antibiotic resistance in tuberculosis to protein-ligand binding to evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models, and convolutional neural networks. We show that SB and MB splits provide an incomplete assessment of model generalizability. +We use SPECTRA with 18 sequencing datasets and phenotypes ranging from antibiotic resistance in tuberculosis to protein–ligand binding and evaluate the generalizability of 19 state-of-the-art deep learning models, including large language models, graph neural networks, diffusion models and convolutional neural networks. We show that sequence similarity- and metadata-based splits provide an incomplete assessment of model generalizability. </p> - <p> -Using SPECTRA, we find as cross-split overlap decreases, deep learning models consistently exhibit a reduction in performance in a task- and model-dependent manner. Although no model consistently achieved the highest performance across all tasks, we show that deep learning models can, in some cases, generalize to previously unseen sequences on specific tasks. SPECTRA paves the way toward a better understanding of how foundation models generalize in biology. +Using SPECTRA, we find that as cross-split overlap decreases, deep learning models consistently show reduced performance, varying by task and model. Although no model consistently achieved the highest performance across all tasks, deep learning models can, in some cases, generalize to previously unseen sequences on specific tasks. SPECTRA advances our understanding of how foundation models generalize in biological applications. </p> </div> @@ -169,15 +162,15 @@ <h2 id="publication">Publication</h2> -<p><a href="https://www.biorxiv.org/content/10.1101/2024.02.25.581982">Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets</a><br /> +<p><a href="https://rdcu.be/d2D0z">Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets</a><br /> Yasha Ektefaie, Andrew Shen, Daria Bykova, Maximillian Marin, Marinka Zitnik* and Maha Farhat*<br /> -<em>In Review</em> 2024 <a href="https://www.biorxiv.org/content/10.1101/2024.02.25.581982">[bioRxiv]</a></p> +<em>Nature Machine Intelligence</em> 2024 <a href="https://www.biorxiv.org/content/10.1101/2024.02.25.581982">[bioRxiv]</a></p> <div class="highlighter-rouge"><div class="highlight"><pre class="highlight"><code>@article{ektefaie2024evaluating, title={Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets}, author={Ektefaie, Yasha and Shen, Andrew and Bykova, Daria and Maximillian, Marin and Zitnik, Marinka* and Farhat, Maha*}, - journal={bioRxiv}, - url={https://www.biorxiv.org/content/10.1101/2024.02.25.581982v1}, + journal={Nature Machine Intelligence}, + url={https://rdcu.be/d2D0z}, year={2024} } </code></pre></div></div> @@ -205,6 +198,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -293,8 +370,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -681,90 +758,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/SubGNN/index.html b/projects/SubGNN/index.html index 38a2dcb1..017f048d 100644 --- a/projects/SubGNN/index.html +++ b/projects/SubGNN/index.html @@ -232,6 +232,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -320,8 +404,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -708,90 +792,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/TF-C/index.html b/projects/TF-C/index.html index 9fbd87f2..25b17861 100644 --- a/projects/TF-C/index.html +++ b/projects/TF-C/index.html @@ -274,6 +274,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -362,8 +446,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -750,90 +834,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/TimeX/index.html b/projects/TimeX/index.html index c108319f..73599cb1 100644 --- a/projects/TimeX/index.html +++ b/projects/TimeX/index.html @@ -235,6 +235,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -323,8 +407,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -711,90 +795,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/TxGNN/index.html b/projects/TxGNN/index.html index 68210ae5..30ee8fed 100644 --- a/projects/TxGNN/index.html +++ b/projects/TxGNN/index.html @@ -223,6 +223,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -311,8 +395,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -699,90 +783,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/UniTS/index.html b/projects/UniTS/index.html index 4ae12a09..9acf5467 100644 --- a/projects/UniTS/index.html +++ b/projects/UniTS/index.html @@ -237,6 +237,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -325,8 +409,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -713,90 +797,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/metapaths/index.html b/projects/metapaths/index.html index 751eae57..d6d421c6 100644 --- a/projects/metapaths/index.html +++ b/projects/metapaths/index.html @@ -199,6 +199,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -287,8 +371,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -675,90 +759,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/patient-safety/index.html b/projects/patient-safety/index.html index 0e9cb850..838b4b03 100644 --- a/projects/patient-safety/index.html +++ b/projects/patient-safety/index.html @@ -317,6 +317,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -405,8 +489,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -793,90 +877,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/projects/scCIPHER/index.html b/projects/scCIPHER/index.html index 3161b39a..a76bc15f 100644 --- a/projects/scCIPHER/index.html +++ b/projects/scCIPHER/index.html @@ -195,6 +195,90 @@ <h2 id="authors">Authors</h2> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -283,8 +367,8 @@ <h2 id="authors">Authors</h2> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -671,90 +755,6 @@ <h2 id="authors">Authors</h2> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/publications/index.html b/publications/index.html index 07d55a58..d4f33151 100644 --- a/publications/index.html +++ b/publications/index.html @@ -163,6 +163,90 @@ <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -251,8 +335,8 @@ <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -639,90 +723,6 @@ </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/publications/thumbnails/ProCyon25.png b/publications/thumbnails/ProCyon25.png new file mode 100644 index 00000000..012ef233 Binary files /dev/null and b/publications/thumbnails/ProCyon25.png differ diff --git a/publications/thumbnails/clinical-knowledge-embeddings.png b/publications/thumbnails/clinical-knowledge-embeddings.png new file mode 100644 index 00000000..80e69cc8 Binary files /dev/null and b/publications/thumbnails/clinical-knowledge-embeddings.png differ diff --git a/pubs.json b/pubs.json index 48b93dec..255a46d2 100644 --- a/pubs.json +++ b/pubs.json @@ -34,6 +34,77 @@ } }, + { + "key": "submission24f", + "author": ["Shanghua Gao", "Ada Fang*", "Yepeng Huang*", "Valentina Giunchiglia*", "Ayush Noori*", "Jonathan Richard Schwarz", "Yasha Ektefaie", "Jovana Kondic", "Marinka Zitnik"], + "title": "Empowering Biomedical Discovery with AI Agents", + "venue": "Cell", + "year": "2024", + "thumbnail": "AIAgents.png", + "pdf": "https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5", + "url": "https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5", + "type": "journal", + "supp": + { + "arXiv": "https://arxiv.org/abs/2404.02831" + } + }, + + { + "key": "submission24u", + "author": ["Owen Queen", "Yepeng Huang", "Robert Calef", "Valentina Giunchiglia", "Tianlong Chen", "George Dasoulas", "LeAnn Tai", "Yasha Ektefaie", "Ayush Noori", "Joseph Brown", "Tom Cobley", "Karin Hrovatin", "Tom Hartvigsen", "Fabian J. Theis", "Bradley L. Pentelute", "Vikram Khurana", "Manolis Kellis", "Marinka Zitnik"], + "title": "ProCyon: A multimodal foundation model for protein phenotypes", + "venue": "In Review", + "year": "2024", + "thumbnail": "ProCyon25.png", + "pdf": "", + "url": "https://www.biorxiv.org/content/10.1101/2024.12.10.627665", + "type": "journal", + "supp": + { + "bioRxiv": "https://www.biorxiv.org/content/10.1101/2024.12.10.627665", + "project website": "https://zitniklab.hms.harvard.edu/ProCyon/", + "code": "https://github.com/mims-harvard/ProCyon" + } + }, + + { + "key": "submission24a", + "author": ["Guadalupe Gonzalez", "Isuru Herath", "Kirill Veselkov", "Michael Bronstein", "Marinka Zitnik"], + "title": "Combinatorial Prediction of Therapeutic Perturbations Using Causally-Inspired Neural Networks", + "venue": "In Review", + "year": "2024", + "thumbnail": "PDGrapher24.png", + "pdf": "", + "url": "https://www.biorxiv.org/content/10.1101/2024.01.03.573985", + "type": "journal", + "supp": + { + "bioRxiv": "https://www.biorxiv.org/content/10.1101/2024.01.03.573985", + "project website": "https://zitniklab.hms.harvard.edu/projects/PDGrapher/", + "code": "https://github.com/mims-harvard/PDGrapher" + } + }, + + { + "key": "submission24t", + "author": ["Ruth Johnson", "Uri Gottlieb", "Galit Shaham", "Lihi Eisen", "Jacob Waxman", "Stav Devons-Sberro", "Curtis R. Ginder", "Peter Hong", "Raheel Sayeed", "Ben Y. Reis", "Ran D. Balicer", "Noa Dagan", "Marinka Zitnik"], + "title": "Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine", + "venue": "In Review", + "year": "2024", + "thumbnail": "clinical-knowledge-embeddings.png", + "pdf": "", + "url": "https://www.medrxiv.org/content/10.1101/2024.12.03.24318322", + "type": "journal", + "supp": + { + "medRxiv": "https://www.medrxiv.org/content/10.1101/2024.12.03.24318322", + "code": "https://github.com/mims-harvard/Clinical-knowledge-embeddings", + "project website": "https://zitniklab.hms.harvard.edu/projects/Clinical-knowledge-embeddings" + } + }, + + { "key": "submission23g", "author": ["Michelle M Li", "Yepeng Huang", "Marissa Sumathipala", "Man Qing Liang", "Alberto Valdeolivas", "Ashwin N Ananthakrishnan", "Katherine Liao", "Daniel Marbach", "Marinka Zitnik"], @@ -80,41 +151,6 @@ } }, - { - "key": "submission24f", - "author": ["Shanghua Gao", "Ada Fang*", "Yepeng Huang*", "Valentina Giunchiglia*", "Ayush Noori*", "Jonathan Richard Schwarz", "Yasha Ektefaie", "Jovana Kondic", "Marinka Zitnik"], - "title": "Empowering Biomedical Discovery with AI Agents", - "venue": "Cell", - "year": "2024", - "thumbnail": "AIAgents.png", - "pdf": "https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5", - "url": "https://www.cell.com/cell/fulltext/S0092-8674(24)01070-5", - "type": "journal", - "supp": - { - "arXiv": "https://arxiv.org/abs/2404.02831" - } - }, - - - { - "key": "submission24a", - "author": ["Guadalupe Gonzalez", "Isuru Herath", "Kirill Veselkov", "Michael Bronstein", "Marinka Zitnik"], - "title": "Combinatorial Prediction of Therapeutic Perturbations Using Causally-Inspired Neural Networks", - "venue": "In Review", - "year": "2024", - "thumbnail": "PDGrapher24.png", - "pdf": "", - "url": "https://www.biorxiv.org/content/10.1101/2024.01.03.573985", - "type": "journal", - "supp": - { - "bioRxiv": "https://www.biorxiv.org/content/10.1101/2024.01.03.573985", - "project website": "https://zitniklab.hms.harvard.edu/projects/PDGrapher/", - "code": "https://github.com/mims-harvard/PDGrapher" - } - }, - { "key": "submission24d", "author": ["Zaixi Zhang", "Wanxiang Shen", "Qi Liu", "Marinka Zitnik"], @@ -137,11 +173,11 @@ "key": "submission24c", "author": ["Yasha Ektefaie", "Andrew Shen", "Daria Bykova", "Maximillian Marin", "Marinka Zitnik*", "Maha R Farhat*"], "title": "Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets", - "venue": "Nature Machine Intelligence (in press)", + "venue": "Nature Machine Intelligence", "year": "2024", "thumbnail": "SPECTRA.png", - "pdf": "", - "url": "https://www.biorxiv.org/content/10.1101/2024.02.25.581982", + "pdf": "https://rdcu.be/d2D0z", + "url": "https://www.nature.com/articles/s42256-024-00931-6", "type": "journal", "supp": { diff --git a/research/index.html b/research/index.html index 22b0a662..87173074 100644 --- a/research/index.html +++ b/research/index.html @@ -333,6 +333,90 @@ <h4 id="initiatives-1">Initiatives:</h4> <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -421,8 +505,8 @@ <h4 id="initiatives-1">Initiatives:</h4> <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -809,90 +893,6 @@ <h4 id="initiatives-1">Initiatives:</h4> </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card"> diff --git a/sitemap.xml b/sitemap.xml index 302ca5c6..c368ab1b 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -581,104 +581,128 @@ <lastmod>2024-11-17T00:00:00-05:00</lastmod> </url> <url> +<loc>https://zitniklab.hms.harvard.edu/2024/12/07/SPECTRA/</loc> +<lastmod>2024-12-07T00:00:00-05:00</lastmod> +</url> +<url> +<loc>https://zitniklab.hms.harvard.edu/2024/12/07/UnifiedClinicalVocabularyEmbeddings/</loc> +<lastmod>2024-12-07T00:00:00-05:00</lastmod> +</url> +<url> +<loc>https://zitniklab.hms.harvard.edu/2024/12/16/ProCyon/</loc> +<lastmod>2024-12-16T00:00:00-05:00</lastmod> +</url> +<url> +<loc>https://zitniklab.hms.harvard.edu/products/aarthi_venkat/</loc> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> +</url> +<url> <loc>https://zitniklab.hms.harvard.edu/products/ada_fang/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/alejandro_velez_arce/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/andrew_shen/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/ayush_noori/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/grey_kuling/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/inaki_arango/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/intae_moon/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> +</url> +<url> +<loc>https://zitniklab.hms.harvard.edu/products/katya_ivshina/</loc> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/kevin_li/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/kexin_chen/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/marinka_zitnik/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> +</url> +<url> +<loc>https://zitniklab.hms.harvard.edu/products/michael_sun/</loc> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/michelle_dai/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/michelle_li/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/pengwei_sui/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/richard_zhu/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/robert_calef/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/ruth_johnson/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/shanghua_gao/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/valentina_giunchiglia/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/wanxiang_shen/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/xiang_lin/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/xiaorui_su/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/yasha_ektefaie/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/yepeng_huang/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/ying_jin/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/products/zhenglun_kong/</loc> -<lastmod>2024-12-02T21:40:57-05:00</lastmod> +<lastmod>2024-12-16T01:28:04-05:00</lastmod> </url> <url> <loc>https://zitniklab.hms.harvard.edu/404/</loc> diff --git a/software/index.html b/software/index.html index 4748c9c0..2247db74 100644 --- a/software/index.html +++ b/software/index.html @@ -148,6 +148,45 @@ </div> + <section class="showcase"> +<!-- <figure class="image is-16by9 ">--> +<!-- <img src="" />--> +<!-- </figure>--> + <div class="showcase-content"> + <div class="columns is-centered"> + <div class="column is-8-desktop is-12-tablet"> + <p class="title">ProCyon</p> + <p class="subtitle">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes.</p> + + + + <div class="content"> + <p> +</p> + </div> + + + + + + + + <a href="https://github.com/mims-harvard/ProCyon" class="button is-primary"> + View ProCyon + </a> + + + <a href="https://zitniklab.hms.harvard.edu/ProCyon/" class="button is-primary"> + ProCyon Website + </a> + + + </div> + </div> + + </div> + </section> + <section class="showcase"> <!-- <figure class="image is-16by9 ">--> <!-- <img src="" />--> @@ -1899,6 +1938,90 @@ <div class="columns is-multiline"> + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/16/ProCyon/">Foundation Model for Protein Phenotypes</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Foundation Model for Protein Phenotypes</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 16, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> +</p>--> + <p>New paper: <a href="https://www.biorxiv.org/content/10.1101/2024.12.10.627665v1">ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes</a>. <a href="https://zitniklab.hms.harvard.edu/ProCyon/">[Project website]</a> <a href="https://github.com/mims-harvard/ProCyon">[Code]</a></p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/16/ProCyon/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 16, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/">Unified Clinical Vocabulary Embeddings</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">Unified Clinical Vocabulary Embeddings</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> +</p>--> + <p>New paper: <a href="https://www.medrxiv.org/content/10.1101/2024.12.03.24318322">A unified resource provides a new representation of clinical knowledge by unifying medical vocabularies.</a> (1) Phenotype risk score analysis across 4.57 million patients, (2) Inter-institutional clinician panels evaluate alignment with clinical knowledge across 90 diseases and 3,000 clinical codes.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/UnifiedClinicalVocabularyEmbeddings/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + + <div class="column is-12"> + <div class="card"> + + <header class="card-header"> +<!-- <a class="card-header-title" href="/2024/12/07/SPECTRA/">SPECTRA in Nature Machine Intelligence</a>--> + <p class="card-header-title">Dec 2024: <span class="has-text-primary">SPECTRA in Nature Machine Intelligence</span></p> +<!-- <p class="card-header-item">Dec 2024</p>--> +<!-- <p class="card-footer-item">Dec 7, 2024</p>--> + </header> + + <div class="card-content"> +<!-- <div class="content">--> +<!-- --> +<!-- <p><p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> +</p>--> + <p>Are biomedical AI models truly as smart as they seem? <a href="https://www.nature.com/articles/s42256-024-00931-6">SPECTRA is a framework that evaluates models by considering the full spectrum of cross-split overlap: train-test similarity.</a> SPECTRA reveals gaps in benchmarks for molecular sequence data across 19 models, including LLMs, GNNs, diffusion models, and conv nets.</p> + +<!-- </div>--> +<!-- <div class="has-text-centered">--> +<!-- <a href="/2024/12/07/SPECTRA/" class="button is-primary">Read more</a>--> +<!-- </div>--> + </div> +<!-- <footer class="card-footer">--> +<!-- <p class="card-footer-item">Published: Dec 7, 2024</p>--> +<!-- </footer>--> +</div> + </div> + <div class="column is-12"> <div class="card"> @@ -1987,8 +2110,8 @@ <div class="card"> <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Property Prediction</a>--> - <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Property Prediction</span></p> +<!-- <a class="card-header-title" href="/2024/10/19/ACAnet/">Activity Cliffs in Molecular Properties</a>--> + <p class="card-header-title">Oct 2024: <span class="has-text-primary">Activity Cliffs in Molecular Properties</span></p> <!-- <p class="card-header-item">Oct 2024</p>--> <!-- <p class="card-footer-item">Oct 19, 2024</p>--> </header> @@ -2375,90 +2498,6 @@ </div> </div> - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/23/EfficientMLSeminar/">Efficient ML Seminar Series</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Efficient ML Seminar Series</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 23, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> -</p>--> - <p>We started a <a href="https://efficientml.org/">Harvard University Efficient ML Seminar Series</a>. Congrats to Jonathan for spearheading this initiative. <a href="https://www.harvardmagazine.com/2024/03/scaling-artificial-intelligence">Harvard Magazine</a> covered the first meeting focusing on LLMs.</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/23/EfficientMLSeminar/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 23, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/04/UniTS/">UniTS - Unified Time Series Model</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">UniTS - Unified Time Series Model</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 4, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> -</p>--> - <p><a href="https://arxiv.org/abs/2403.00131">UniTS is a unified time series model</a> that can process classification, forecasting, anomaly detection and imputation tasks within a single model with no task-specific modules. UniTS has zero-shot, few-shot, and prompt learning capabilities. <a href="https://zitniklab.hms.harvard.edu/projects/UniTS/">Project website.</a></p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/04/UniTS/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 4, 2024</p>--> -<!-- </footer>--> -</div> - </div> - - <div class="column is-12"> - <div class="card"> - - <header class="card-header"> -<!-- <a class="card-header-title" href="/2024/03/02/WeintraubAward/">Weintraub Graduate Student Award</a>--> - <p class="card-header-title">Mar 2024: <span class="has-text-primary">Weintraub Graduate Student Award</span></p> -<!-- <p class="card-header-item">Mar 2024</p>--> -<!-- <p class="card-footer-item">Mar 2, 2024</p>--> - </header> - - <div class="card-content"> -<!-- <div class="content">--> -<!-- --> -<!-- <p><p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> -</p>--> - <p>Michelle receives the 2024 Harold M. Weintraub Graduate Student Award. The award recognizes exceptional achievement in graduate studies in biological sciences. <a href="https://dbmi.hms.harvard.edu/news/li-receives-weintraub-graduate-student-award">News Story.</a> Congratulations!</p> - -<!-- </div>--> -<!-- <div class="has-text-centered">--> -<!-- <a href="/2024/03/02/WeintraubAward/" class="button is-primary">Read more</a>--> -<!-- </div>--> - </div> -<!-- <footer class="card-footer">--> -<!-- <p class="card-footer-item">Published: Mar 2, 2024</p>--> -<!-- </footer>--> -</div> - </div> - <div class="column is-12"> <div class="card">