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<!doctype html>
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<meta charset="utf-8">
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<title>Michael D. Kessler</title>
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<h1 style="font-weight:200">Michael D. Kessler</h1>
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<p style="font-size:16px; font-weight:200; line-height: 24px; margin-top: 0px; font-family:Helvetica">
I am a bioinformatic scientist with wide ranging experience in oncology, genetics and genomics.
<!-- Currently, I am working on cancer genomic research projects as a postdoctoral fellow in the <a href="https://www.rits.onc.jhmi.edu/dbb/">Oncology Division of Biostatistics and Bioinformatics</a> at The <a href="https://www.hopkinsmedicine.org">Johns Hopkins University School of Medicine</a>!-->
<!-- I am currently Postdoctoral Researcher in Oncological Data Science at <a href="https://www.jhu.edu">Johns Hopkins University</a>Johns Hopkins University in Baltimore, Maryland, USA, working with Professor <a href="https://cmm.jhmi.edu/index.php/cmm-faculty/elana-j-fertig-phd/">Elana Fertig</a>. -->
<br/>
<br/>
My research interests lie at the intersection of human genetics, cancer genomics, and translational bioinformatics, and I strive to leverage data science tools and techniques to inform increasingly personalized and precise medical care.
<!--
My name is Michael D. Kessler, and I am currently a postdoctoral fellow working on bioinformatic and cancer genomic research in the Department of Oncology at The Johns Hopkins University School of Medicine. Prior to this position, I studied cancer biology and genome science as a PhD student in Molecular Medicine at the University of Maryland School of Medicine. I am currently working on pan cancer and head and neck cancer specific biological data science projects related to transcription factors, super enhancers, alternative slplicing, and high throughput drug sensitivity screens. I previously worked on projects related to population genetics and molecular evolution, and my thesis work focused on the application of clinical genomics to human populations with differing ancestral backgrounds. I am interested in using data driven analysis to find answers to biomedical questions of translational, financial, and industrial interest.
<p ><h6><b><font color="#b2182b">I started a postdoc at FGV-Emap Rio de Janeiro, Brasil in February 2020 ;)!</h6></font></b></p>
<p ><h5><b>Recent news</b></h5>
<ul style="font-size:14px; font-weight:350">
<li> <b> August 2020 </b> I have joined the <a href="http://www.cov-irt.org/">COVID-19 International Research Team (COV-IRT)</a> organised by NASA. COV-IRT is a multidisciplinary open-science research consortium and non-profit organization focused on COVID-19 research.
</li>
<li> <b> July 2020 </b> Our paper on Predicting the Frequencies of drug Side effects has been accepted at Nature Communications! This was the main work of my Ph.D. research.
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<!-- <li class="tabs-title"><a href="#panel2" style="font-size:20px; color:grey">Projects</a></li> !-->
<!-- <li class="tabs-title"><a href="#panel3" style="font-size:20px; color:grey">About</a></li> !-->
<!-- <li class="tabs-title"><a href="#panel3" style="font-size:20px; color:grey">Collaborators</a></li> !-->
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<h5>2020</h5>
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<img class='paperimage' alt="" src="images/papers/Erbe_et_al.NAR.2020.jpg"/>
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<a class='papertitle' href="https://academic.oup.com/nar/article/48/12/e68/5835820">Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets</a><br/>
Rossin Erbe, <span style="font-weight:400">Michael D. Kessler</span>, Alexander V. Favorov, Hariharan Easwaran, Daria A. Gaykalova, Elana J. Fertig<br/>
<i>Nucleic Acids Research</i> 2020
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<a class='papertitle' href="https://www.pnas.org/content/117/17/9458/tab-figures-data">Evolutionary history of modern Samoans</a><br/>
Daniel N. Harris, <span style="font-weight:400">Michael D. Kessler</span>, Amol C. Shetty, Daniel E. Weeks, Ryan L. Minster, Sharon Browning, Ethan E. Cochrane, Ranjan Deka, Nicola L. Hawley, Muagututi‘a Sefuiva Reupena, Take Naseri, <a href="https://www.nhlbiwgs.org">Trans-Omics for Precision Medicine (TOPMed) Consortium</a>, TOPMed Population Genetics Working Group, Stephen T. McGarvey, and Timothy D. O’Connor<br/>
<i>Proceedings of the National Academy of Sciences</i> 2020
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<a class='papertitle' href="https://www.pnas.org/content/117/5/2560">De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population</a><br/>
<span style="font-weight:400">Michael D. Kessler</span>, Douglas P. Loesch, James A. Perry, Nancy L. Heard-Costa, Daniel Taliun, Brian E. Cade, Heming Wang, Michelle Daya, John Ziniti, Soma Datta, Juan C. Celedón, Manuel E. Soto-Quiros, Lydiana Avila, Scott T. Weiss, Kathleen Barnes, Susan S. Redline, Ramachandran S. Vasan, Andrew D. Johnson, Rasika A. Mathias, Ryan Hernandez, James G. Wilson, Deborah A. Nickerson, Goncalo Abecasis, Sharon R. Browning, Sebastian Zöllner, Jeffrey R. O’Connell, Braxton D. Mitchell, <a href="https://www.nhlbiwgs.org">National Heart, Lung,, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Consortium</a>, TOPMed Population Genetics Working Group, and Timothy D. O’Connor<br/>
<i>Proceedings of the National Academy of Sciences<i/> 2020
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<h5>2019</h5>
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<a class='papertitle' href="https://acsjournals.onlinelibrary.wiley.com/doi/full/10.1002/cncr.32020">Ancestral characterization of 1018 cancer cell lines highlights disparities and reveals gene expression and mutational differences</a><br/>
<span style="font-weight:400">Michael D. Kessler</span>, Nicholas W. Bateman, Thomas P. Conrads, George L. Maxwell, Julie C. Dunning Hotopp, Timothy D. O’Connor<br/>
<i>Cancer</i> 2019
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<a class='papertitle' href="https://www.biorxiv.org/content/10.1101/563866v1.abstract">Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program</a><br/>
(co-first author*) Daniel Taliun*, Daniel N. Harris*, <span style="font-weight:400">Michael D. Kessler*</span>, Jedidiah Carlson*, Zachary A. Szpiech*, Raul Torres*, Sarah A. Gagliano Taliun*, André Corvelo*, Stephanie M. Gogarten, Hyun Min Kang, Achilleas N. Pitsillides, Jonathon LeFaive, Seung-been Lee, Xiaowen Tian, Brian L. Browning, Sayantan Das, Anne-Katrin Emde, Wayne E. Clarke, Douglas P. Loesch, Amol C. Shetty, Thomas W. Blackwell, Quenna Wong, François Aguet, Christine Albert, Alvaro Alonso, Kristin G. Ardlie, Stella Aslibekyan, Paul L. Auer, John Barnard, R. Graham Barr, Lewis C. Becker, Rebecca L. Beer, Emelia J. Benjamin, Lawrence F. Bielak, John Blangero, Michael Boehnke, Donald W. Bowden, Jennifer A. Brody, Esteban G. Burchard, Brian E. Cade, James F. Casella, Brandon Chalazan, Yii-Der Ida Chen, Michael H. Cho, Seung Hoan Choi, Mina K. Chung, Clary B. Clish, Adolfo Correa, Joanne E. Curran, Brian Custer, Dawood Darbar, Michelle Daya, Mariza de Andrade, Dawn L. DeMeo, Susan K. Dutcher, Patrick T. Ellinor, Leslie S. Emery, Diane Fatkin, Lukas Forer, Myriam Fornage, Nora Franceschini, Christian Fuchsberger, Stephanie M. Fullerton, Soren Germer, Mark T. Gladwin, Daniel J. Gottlieb, Xiuqing Guo, Michael E. Hall, Jiang He, Nancy L. Heard-Costa, Susan R. Heckbert, Marguerite R. Irvin, Jill M. Johnsen, Andrew D. Johnson, Sharon L.R. Kardia, Tanika Kelly, Shannon Kelly, Eimear E. Kenny, Douglas P. Kiel, Robert Klemmer, Barbara A. Konkle, Charles Kooperberg, Anna Köttgen, Leslie A. Lange, Jessica Lasky-Su, Daniel Levy, Xihong Lin, Keng-Han Lin, Chunyu Liu, Ruth J.F. Loos, Lori Garman, Robert Gerszten, Steven A. Lubitz, Kathryn L. Lunetta, Angel C.Y. Mak, Ani Manichaikul, Alisa K. Manning, Rasika A. Mathias, David D. McManus, Stephen T. McGarvey, James B. Meigs, Deborah A. Meyers, Julie L. Mikulla, Mollie A. Minear, Braxton Mitchell, Sanghamitra Mohanty, May E. Montasser, Courtney Montgomery, Alanna C. Morrison, Joanne M. Murabito, Andrea Natale, Pradeep Natarajan, Sarah C. Nelson, Kari E. North, Jeffrey R. O’Connell, Nicholette D. Palmer, Nathan Pankratz, Gina M. Peloso, Patricia A. Peyser, Wendy S. Post, Bruce M. Psaty, D.C. Rao, Susan Redline, Alexander P. Reiner, Dan Roden, Jerome I. Rotter, Ingo Ruczinski, Chloé Sarnowski, Sebastian Schoenherr, Jeong-Sun Seo, Sudha Seshadri, Vivien A. Sheehan, M. Benjamin Shoemaker, Albert V. Smith, Nicholas L. Smith, Jennifer A. Smith, Nona Sotoodehnia, Adrienne M. Stilp, Weihong Tang, Kent D. Taylor, Marilyn Telen, Timothy A. Thornton, Russell P. Tracy, David J. Van Den Berg, Ramachandran S. Vasan, Karine A. Viaud-Martinez, Scott Vrieze, Daniel E Weeks, Bruce S. Weir, Scott T. Weiss, Lu-Chen Weng, Cristen J. Willer, Yingze Zhang, Xutong Zhao, Donna K. Arnett, Allison E. Ashley-Koch, Kathleen C. Barnes, Eric Boerwinkle, Stacey Gabriel, Richard Gibbs, Kenneth M. Rice, Stephen S. Rich, Edwin Silverman, Pankaj Qasba, Weiniu Gan, Trans-Omics for Precision Medicine (TOPMed) Program, TOPMed Population Genetics Working Group</a>, George J. Papanicolaou, Deborah A. Nickerson, Sharon R. Browning, Michael C. Zody, Sebastian Zöllner, James G. Wilson, L Adrienne Cupples, Cathy C. Laurie, Cashell E. Jaquish, Ryan D. Hernandez, Timothy D. O’Connor, Gonçalo R. Abecasis<br/>
<i>Preprint 2019</i>
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<h5>2018</h5>
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<a class='papertitle' href="https://www.sciencedirect.com/science/article/abs/pii/S2405803318301572">Improving Cancer Detection and Treatment with Liquid Biopsies and ptDNA</a><br/>
<span style="font-weight:400">Michael D. Kessler</span>, Nisha R.Pawar, Stuart S. Martin, Toni M. Antalis, Timothy D. O’Connor<br/>
<i>Trends in Cancer</i> 2018
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<a class='papertitle' href="https://www.pnas.org/content/115/28/E6526.short">Evolutionary genomic dynamics of Peruvians before, during, and after the Inca Empire</a><br/>
Daniel N. Harris, Wei Song, Amol C. Shetty, Kelly S. Levano, Omar Cáceres, Carlos Padilla, Víctor Borda, David Tarazona, Omar Trujillo, Cesar Sanchez, <span style="font-weight:400">Michael D. Kessler</span>, Marco Galarza, Silvia Capristano, Harrison Montejo, Pedro O. Flores-Villanueva, Eduardo Tarazona-Santos, Timothy D. O’Connor, and Heinner Guio<br/>
<i>Proceedings of the National Academy of Sciences<i/> 2018
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<a class='papertitle' href="https://www.genetics.org/content/209/3/845.short">The Evolution of Polymorphic Hybrid Incompatibilities in House Mice</a><br/>
Erica L. Larson, Dan Vanderpool, Brice A. J. Sarver, Colin Callahan, Sara Keeble, Lorraine L. Provencio, <span style="font-weight:400">Michael D. Kessler</span>, Vanessa Stewart, Erin Nordquist, Matthew D. Dean, Jeffrey M. Good<br/>
<i>Genetics</i> 2018
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<h5>2017</h5>
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<a class='papertitle' href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1172-8">Accurate and equitable medical genomic analysis requires an understanding of demography and its influence on sample size and ratio</a><br/>
<span style="font-weight:400">Michael D. Kessler</span> and Timothy D. O'Connor<br/>
<i>Genome Biology</i> 2017
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<a class='papertitle' href="https://www.sciencedirect.com/science/article/pii/S088539241730297X">Oxaliplatin-Induced Peripheral Neuropathy and Identification of Unique Severity Groups in Colorectal Cancer</a><br/>
Kathleen A. Griffith, Shijun Zhu, Meg Johantgen, <span style="font-weight:400">Michael D.Kessler</span>, Cynthia Renn, Andreas S. Beutler, Rahul Kanwar, Nicholas Ambulos, Guido Cavaletti, Jordi Bruna, Chiara Briani, Andreas A. Argyriou, Haralabos P. Kalofonos, Laura M.Yerges-Armstrong, Susan G. Dorsey<br/>
<i>Journal of Pain and Symptom Management</i> 2017
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<h5>2016</h5>
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<a class='papertitle' href="https://www.nature.com/articles/ncomms12521">Challenges and disparities in the application of personalized genomic medicine to populations with African ancestry</a><br/>
<span style="font-weight:400">Michael D. Kessler</span>, Laura Yerges-Armstrong, Margaret A. Taub, Amol C. Shetty, Kristin Maloney, Linda Jo Bone Jeng, Ingo Ruczinski, Albert M. Levin, L. Keoki Williams, Terri H. Beaty, Rasika A. Mathias, Kathleen C. Barnes, Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) & Timothy D. O’Connor<br/>
<i>Nature Communications</i> 2016
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<h5>2015</h5>
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<a class='papertitle' href="https://www.sciencedirect.com/science/article/abs/pii/S030645221500740X">The placebo effect: From concepts to genes</a><br/>
B. Colagiuri, L. A. Schenk, <span style="font-weight:400">Michael D. Kessler</span> S. G. Dorsey, L. Colloca<br/>
<i>Neuroscience</i> 2015
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<h5>2014</h5>
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<a class='papertitle' href="https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.1249">Effective population size does not predict codon usage bias in mammals</a><br/>
<span style="font-weight:400">Michael D. Kessler</span> and Matthew D. Dean<br/>
<i>Ecology and Evolution</i> 2014
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<h5>2013</h5>
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<a class='papertitle' href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0081148">A Reference Methylome Database and Analysis Pipeline to Facilitate Integrative and Comparative Epigenomics</a><br/>
Qiang Song, Benjamin Decato, Elizabeth E. Hong, Meng Zhou, Fang Fang, Jianghan Qu, Tyler Garvin, <span style="font-weight:400">Michael D. Kessler</span>, Jun Zhou, Andrew D. Smith<br/>
<i>PLOS ONE</i> 2013
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<h4>1 - Title of some first analysis</h4>
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A bunch of description with a link to a <a <href="https://github.com/mdkessler/kTSPR">GitHub repo</a><br/>
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<h4>Title of some second analysis</h4>
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<h4>1 - Predicting the frequencies of drug side effects</h4>
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A central issue in drug risk-benefit assessment is the identification of the frequencies of side effects in humans. Currently, these frequencies are experimentally determined in randomised controlled clinical trials. We developed a novel machine learning framework for computationally predicting the frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures of drugs and side effects that are both reproducible and biologically interpretable. We show the usefulness of our approach on 759 structurally and therapeutically diverse drugs and 994 side effects from all human physiological systems. Our approach can be applied to any drug or compound for which a few side effect frequencies have been identified, in order to predict the frequencies of further, yet unidentified, side effects. We also found that our model is informative of the biology underlying drug activity: individual components of the drug signatures are related to the distinct anatomical categories of the drugs and to the specific drug routes of administration.
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<h4>2 - Extending the druggable genome</h4>
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The identification of unknown targets of drugs is critical for the development of novel medicines and for the repositioning of old drugs. Yet, the molecular characterisation of molecules remains a daunting task. Recent estimates indicates that the number of targets known for approved drugs is only 4.5 (DrugBank 5.1). The goal of this project is to develop a machine learning model that not only allow to predict novel drug targets but also, it can help us to extend the known druggable genome.
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<a class='papertitle' href="" style="margin-bottom: 15px; margin-top: 15px"><b>References</b></a><br/>
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<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 1em;">1.</div><div class="csl-right-inline" style="margin: 0 .4em 0 1.5em;">Hopkins, A. L. & Groom, C. R. The druggable genome. <i>Nature Reviews Drug Discovery</i> <b>1</b>, 727–730 (2002).</div>
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<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 1em;">1.</div><div class="csl-right-inline" style="margin: 0 .4em 0 1.5em;">Finan, C. <i>et al.</i> The druggable genome and support for target identification and validation in drug development. <i>Science Translational Medicine</i> <b>9</b>, (2017).</div>
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<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 1em;">1.</div><div class="csl-right-inline" style="margin: 0 .4em 0 1.5em;">Russ, A. P. & Lampel, S. The druggable genome: an update. <i>Drug Discovery Today</i> <b>10</b>, 1607–1610 (2005).</div>
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<div class="csl-left-margin" style="float: left; padding-right: 0.5em;text-align: right; width: 1em;">1.</div><div class="csl-right-inline" style="margin: 0 .4em 0 1.5em;">Rask-Andersen, M., Masuram, S. & Schiöth, H. B. The Druggable Genome: Evaluation of Drug Targets in Clinical Trials Suggests Major Shifts in Molecular Class and Indication. <i>Annu. Rev. Pharmacol. Toxicol.</i> <b>54</b>, 9–26 (2014).</div>
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<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Our paper is finally out! It has been a long journey! Thanks to my supervisor <a href="https://twitter.com/A_Paccanaro?ref_src=twsrc%5Etfw">@A_Paccanaro</a> and co-authors <a href="https://twitter.com/shantaolee?ref_src=twsrc%5Etfw">@shantaolee</a> <a href="https://twitter.com/MarkGerstein?ref_src=twsrc%5Etfw">@MarkGerstein</a>! Please share our work <a href="https://twitter.com/hashtag/drugsideeffects?src=hash&ref_src=twsrc%5Etfw">#drugsideeffects</a> <a href="https://twitter.com/hashtag/drugsafety?src=hash&ref_src=twsrc%5Etfw">#drugsafety</a> <a href="https://twitter.com/hashtag/machinelearning?src=hash&ref_src=twsrc%5Etfw">#machinelearning</a> <a href="https://twitter.com/hashtag/AI?src=hash&ref_src=twsrc%5Etfw">#AI</a> <a href="https://twitter.com/NatureComms?ref_src=twsrc%5Etfw">@NatureComms</a> <a href="https://t.co/mftdHUXokF">https://t.co/mftdHUXokF</a></p>— diego galeano (@diegogaleano05) <a href="https://twitter.com/diegogaleano05/status/1304450950124445696?ref_src=twsrc%5Etfw">September 11, 2020</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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