ArticleMolecular systems biology2023
Predicting cellular responses to complex perturbations in high-throughput screens.
Article in Molecular systems biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers.
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Who cites it
159 citing papers in PubMed.
- Host chromatin architecture in HPV-driven cervical cancer: molecular mechanisms and therapeutic resistance.Medical oncology (Northwood, London, England) · 2026Review
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- Single-cell RNA profiling suggests goflikicept-mediated immune modulation in idiopathic recurrent pericarditis.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- PHAROS: turning single-cell perturbation models into target-directed drug-combination screens.bioRxiv : the preprint server for biology · 2026Article
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- Post-genome-wide association study variant-to-function challenges in asthma research.The Journal of allergy and clinical immunology · 2026Review
- scBalFlow: a staged flow matching framework for imbalanced single-cell drug perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- A framework to map cAMP signalling nanodomains: phosphodiesterase-centred phosphoproteome-interactome networks (pPINs) in cardiac myocytes.Journal of molecular endocrinology · 2026Review
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- ProteinNetworkSight: a user-friendly platform for transforming co-expression patterns into actionable therapeutic insights through interactive network visualization.Nucleic acids research · 2026Article
- Synthesizing Mechanistic Hypotheses from Single-Cell Omics via Discretized Feature Attribution and Empirical Language Model Grounding.bioRxiv : the preprint server for biology · 2026Article
- PertAdapt: unlocking single-cell foundation models for genetic perturbation prediction via condition-sensitive adaptation.Bioinformatics (Oxford, England) · 2026Article
- Synthetic developmental engineering of human liver organogenesis.Development (Cambridge, England) · 2026Review
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- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation.Nature biotechnology · 2026Article
99 more citing papers are in PubMed but not listed here.
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19 authors.
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Abstract
Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.