Many models for single-cell perturbation data coming out!
| Name | Year | Journal | Title |
|---|---|---|---|
| Rachel et al | 2018 | Pacific Symposium on Biocomputing 2018 | Cell-specific prediction and application of drug-induced gene expression profiles |
| scGEN | 2019 | Nature Method | scGen predicts single-cell perturbation responses |
| DTD | 2019 | The World Wide Web Conference, 2019 | Modeling Relational Drug-Target-Disease Interactions via Tensor Factorization with Multiple Web Sources |
| CPA | 2021 | Molecular system biology | Predicting cellular responses to complex perturbations in high‐throughput screens |
| CellBox | 2021 | Cell systems | CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy |
| CellDrift | 2022 | BIB | CellDrift: inferring perturbation responses in temporally sampled single-cell data |
| MultiCPA | 2022 | MultiCPA: Multimodal Compositional Perturbation Autoencoder | |
| PerturbNet | 2022 | PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations | |
| scINSIGHT | 2022 | Genome biology | scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data |
| scpregan | 2022 | Bioinformatics | scPreGAN, a deep generative model for predicting the response of single-cell expression to perturbation |
| Gears | 2023 | Nature Biotech | Predicting transcriptional outcomes of novel multigene perturbations with GEARS |
| cycleCDR | 2023 | Interpretable Modeling of Single-cell perturbation Responses to Novel Drugs Using Cycle Consistence Learning | |
| scVIDR | 2023 | Patterns | Generative modeling of single-cell gene expression for dose-dependent chemical perturbations |
| Unagi | 2023 | Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases | |
| CINEMA-OT | 2023 | Nature Method | Causal identification of single-cell experimental perturbation effects with CINEMA-OT |
| ChemCPA | 2023 | NeurIPS 2022 | Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution |
| DREEP | 2023 | BMC Medicine | Predicting drug response from single-cell expression profiles of tumours |
| ontoVAE | 2023 | Bioinformatics | Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations |
| scDiff | 2023 | A GENERAL SINGLE-CELL ANALYSIS FRAMEWORK VIA CONDITIONAL DIFFUSION GENERATIVE MODELS | |
| ContrastiveVI | 2023 | Nature Method | Isolating salient variations of interest in single-cell data with contrastiveVI |
| sVAE | 2023 | PMLR | Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling |
| CellOT | 2023 | Nature Method | Learning single-cell perturbation responses using neural optimal transport |
| MOASL | 2023 | Computers in Biology and Medicine | MOASL: Predicting drug mechanism of actions through similarity learning with transcriptomic signature |
| samsVAE | 2024 | Advances in Neural Information Processing Systems | Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder |
| Biolord | 2024 | Nature Biotech | Disentanglement of single-cell data with biolord |
| Pdgrapher | 2024 | Combinatorial prediction of therapeutic perturbations using causally-inspired neural networks | |
| TAT | 2024 | Journal of Chemical Information and Modeling | Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning |
| scVAE | 2024 | A Supervised Contrastive Framework for Learning Disentangled Representations of Cell Perturbation Data | |
| Cell PaintingCNN | 2024 | NC | Learning representations for image-based profiling of perturbations |
| scDisInFact | 2024 | NC | scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data |
| CellCap | 2024 | Modeling interpretable correspondence between cell state and perturbation response with CellCap | |
| CODEX | 2024 | Bioinformatics | CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations |
| scFM | 2024 | PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction | |
| STAMP | 2024 | NCS | Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond |
| PrePR-CT | 2024 | PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs | |
| PRnet | 2024 | NC | Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery |
| TranSiGen | 2024 | NC | Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery |
| BioDiscoveryAgent | 2024 | BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments | |
| DRSPRING | 2024 | Computers in Biology and Medicine | DRSPRING: Graph convolutional network (GCN)-Based drug synergy prediction utilizing drug-induced gene expression profile |
| PertKGE | 2024 | Identify compound-protein interaction with knowledge graph embedding of perturbation transcriptomics | |
| scRank | 2024 | Cell Reports Medicine | scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network |
| Pertpy | 2024 | Pertpy: an end-to-end framework for perturbation analysis |
SC-perturb
C-MAP
PerturbBase
PerturbDB
Multiome Perturb-seq (paper)
Multiome: CRISPRmap (paper)
Spatial: Perturb-Fish (paper)
Spatial: PerturbView (paper)
Spatial: Perturb-map (paper)
| Name | Year | Journal | Title |
|---|---|---|---|
| DeepMAPS | 2021 | NC | DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer |
| scBERT | 2022 | NMI | scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data |
| TransCluster | 2022 | Frontiers in Genetics | TransCluster: A Cell-Type Identification Method for single-cell RNA-Seq data using deep learning based on transformer. |
| scMVP | 2022 | Genome Biology | A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data |
| scGPT | 2023 | NM | scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI |
| Geneformer | 2023 | Nature | Transfer learning enables predictions in network biology |
| CellLM | 2023 | Large-Scale Cell Representation Learning via Divide-and-Conquer Contrastive Learning | |
| Tgpt | 2023 | Iscience | Generative pretraining from large-scale transcriptomes for single-cell deciphering |
| Scimilarity | 2023 | Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages | |
| scFoundation | 2023 | Large Scale Foundation Model on Single-cell Transcriptomics. | |
| TOSICA | 2023 | NC | Transformer for one stop interpretable cell type annotation |
| CIForm | 2023 | BIB | CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data |
| scTransSort | 2023 | Biomolecules | scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings |
| scMoFormer | 2023 | ICIKM | Single-Cell Multimodal Prediction via Transformers |
| scTranslator | 2023 | A pre-trained large generative model for translating single-cell transcriptome to proteome | |
| Cell2Sentence | 2023 | Cell2Sentence: Teaching Large Language Models the Language of Biology | |
| genePT | 2023 | GENEPT: A SIMPLE BUT HARD-TO-BEAT FOUNDATION MODEL FOR GENES AND CELLS BUILT FROM CHATGPT | |
| scMulan | 2024 | ICRCMB | scMulan: a multitask generative pre-trained language model for single-cell analysis. |
| Nicheformer | 2024 | Nicheformer: a foundation model for single-cell and spatial omics. |