Research graph
References from A mini-review on perturbation modelling across single-cell omic modalities. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1016/j.cels.2021.05.016
10.1016/j.cels.2021.05.016 · External reference
10.1016/j.cell.2017.10.049
10.1016/j.cell.2017.10.049 · External reference
DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery
10.1038/s41467-018-06500-x · 2018 · External reference
10.1016/j.cels.2021.05.016
10.1016/j.cels.2021.05.016 · External reference
Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning
10.1093/database/baab017 · 2021 · External reference
Massively parallel CRISPR-based genetic perturbation screening at single-cell resolution
10.1002/advs.202204484 · 2023 · External reference
10.1016/j.cell.2016.11.038
10.1016/j.cell.2016.11.038 · External reference
10.1016/j.cell.2016.11.039
10.1016/j.cell.2016.11.039 · External reference
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10.1038/nmeth.4177 · External reference
10.1186/s13059-020-02044-w
10.1186/s13059-020-02044-w · External reference
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10.1038/s41467-020-17440-w · External reference
10.1126/science.aax6234
10.1126/science.aax6234 · External reference
10.1038/s41592-020-0837-5
10.1038/s41592-020-0837-5 · External reference
10.1016/j.cell.2020.03.039
10.1016/j.cell.2020.03.039 · External reference
10.1038/s41592-019-0392-0
10.1038/s41592-019-0392-0 · External reference
10.1038/s41588-021-00779-1
10.1038/s41588-021-00779-1 · External reference
10.1016/j.molcel.2017.03.007
10.1016/j.molcel.2017.03.007 · External reference
10.1038/s41467-021-24324-0
10.1038/s41467-021-24324-0 · External reference
10.1016/j.cell.2018.11.022
10.1016/j.cell.2018.11.022 · External reference
High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer
10.1038/s41467-021-23213-w · 2021 · External reference
10.1038/s41587-021-00902-x
10.1038/s41587-021-00902-x · External reference
10.1038/s41587-021-01160-7
10.1038/s41587-021-01160-7 · External reference
10.1016/j.molcel.2023.03.013
10.1016/j.molcel.2023.03.013 · External reference
A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens
10.1186/s12864-023-09754-y · 2023 · External reference
Systematic functional interrogation of SARS-CoV-2 host factors using Perturb-seq
10.1038/s41467-023-41788-4 · 2023 · External reference
scPerturb: harmonized single-cell perturbation data
2024 · External reference
Model-based understanding of single-cell CRISPR screening
10.1038/s41467-019-10216-x · 2019 · External reference
scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens
10.1186/s13059-020-1928-4 · 2020 · External reference
10.1186/s13059-019-1874-1
10.1186/s13059-019-1874-1 · External reference
SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis
10.1186/s13059-021-02545-2 · 2021 · External reference
10.1038/s41587-020-0605-1
10.1038/s41587-020-0605-1 · External reference
10.1101/2023.05.06.539326
10.1101/2023.05.06.539326 · External reference
ASGARD is a single-cell guided pipeline to aid repurposing of drugs
10.1038/s41467-023-36637-3 · 2023 · External reference
10.1038/s41587-021-01033-z
10.1038/s41587-021-01033-z · External reference
10.1038/s41588-021-00778-2
10.1038/s41588-021-00778-2 · External reference
10.1038/s41587-020-00803-5
10.1038/s41587-020-00803-5 · External reference
CellDrift: inferring perturbation responses in temporally sampled single-cell data
10.1093/bib/bbac324 · 2022 · External reference
ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes
10.1093/bioinformatics/btad278 · 2023 · External reference
A unified computational framework for single-cell data integration with optimal transport
10.1038/s41467-022-35094-8 · 2022 · External reference
10.1038/s41592-023-02040-5
10.1038/s41592-023-02040-5 · External reference
Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures
10.1186/s13073-021-01000-y · 2021 · External reference
10.1038/s41592-023-02144-y
10.1038/s41592-023-02144-y · External reference
NetBID2 provides comprehensive hidden driver analysis
10.1038/s41467-023-38335-6 · 2023 · External reference
10.1038/s41586-022-05688-9
10.1038/s41586-022-05688-9 · External reference
10.1016/j.stemcr.2022.11.010
10.1016/j.stemcr.2022.11.010 · External reference
10.1101/2023.04.19.537364
10.1101/2023.04.19.537364 · External reference
RENGE infers gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations
10.1038/s42003-023-05594-4 · 2023 · External reference
10.1038/s41592-023-01938-4
10.1038/s41592-023-01938-4 · External reference
10.1038/nmeth.4463
10.1038/nmeth.4463 · External reference
SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics
10.1016/j.isci.2023.107124 · 2023 · External reference
Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks
10.1093/nar/gkad450 · 2023 · External reference
10.1038/s41592-019-0494-8
10.1038/s41592-019-0494-8 · External reference
10.1093/bioinformatics/btaa800
10.1093/bioinformatics/btaa800 · External reference
Predicting cellular responses to complex perturbations in high‐throughput screens.
10.15252/msb.202211517 · 2023 · External reference
10.1101/2022.07.08.499049
10.1101/2022.07.08.499049 · External reference
VEGA is an interpretable generative model for inferring biological network activity in single-cell transcriptomics
10.1038/s41467-021-26017-0 · 2021 · External reference
10.1016/j.patter.2023.100817
10.1016/j.patter.2023.100817 · External reference
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Predicting cellular responses with variational causal inference and refined relational information
2023 · External reference
Biologically informed deep learning to query gene programs in single-cell atlases
10.1038/s41556-022-01072-x · 2023 · External reference
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10.1038/s41467-021-23518-w
10.1038/s41467-021-23518-w · External reference
Predicting transcriptional outcomes of novel multigene perturbations with GEARS
2023 · External reference
10.1101/2022.07.20.500854
10.1101/2022.07.20.500854 · External reference
10.1093/bioinformatics/btad387
10.1093/bioinformatics/btad387 · External reference
10.1101/2023.07.25.550225
10.1101/2023.07.25.550225 · External reference
10.21203/rs.3.rs-3676579/v1
10.21203/rs.3.rs-3676579/v1 · External reference
Geneformer: a deep learning model for exploring gene networks
10.1007/s11427-023-2431-x · 2023 · External reference
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
10.1038/s41592-024-02201-0 · 2024 · External reference
10.1101/2023.12.07.569910
10.1101/2023.12.07.569910 · External reference
10.1101/2023.05.29.542705
10.1101/2023.05.29.542705 · External reference
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Mechanism-aware and multimodal AI: beyond model-agnostic interpretation
2023 · External reference
10.1016/j.cell.2022.02.015
10.1016/j.cell.2022.02.015 · External reference
10.1038/s41586-020-2715-9
10.1038/s41586-020-2715-9 · External reference
10.1016/j.cell.2023.01.002
10.1016/j.cell.2023.01.002 · External reference
10.1101/2023.10.16.561085
10.1101/2023.10.16.561085 · External reference
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10.1101/2023.07.17.549216
10.1101/2023.07.17.549216 · External reference
10.1023/a:1007379606734
10.1023/a:1007379606734 · External reference
Explainable multi-task learning for multi-modality biological data analysis
10.1038/s41467-023-37477-x · 2023 · External reference
Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations
10.1038/s41592-024-02241-6 · 2024 · External reference
10.1038/s41573-023-00688-4
10.1038/s41573-023-00688-4 · External reference
