ArticleNature methods2026
Benchmarking algorithms for generalizable single-cell perturbation response prediction.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Review
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction networks.Genome medicine · 2026Article
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- Spurious correlation inflates performance in single-cell perturbation prediction.bioRxiv : the preprint server for biology · 2026Article
- pertTF: context-aware AI modeling for genome-scale and cross-system perturbation prediction.bioRxiv : the preprint server for biology · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Virtual Cells Need Context, Not Just Scale.bioRxiv : the preprint server for biology · 2026Article
- Grand challenges for systems neuroscience: perspectives and opportunities.Frontiers in systems neuroscience · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
Single-cell perturbation technologies enable systematic investigation of gene functions and regulatory networks with single-cell resolution. However, performing large-scale and combinatorial perturbation screens poses notable challenges due to their exponentially increased complexity. Computational methods, including foundation models, have been developed to predict perturbation effects. Yet despite claims of promising performance, concerns remain about their true efficacy, particularly when evaluated across diverse and previously unseen cellular contexts and perturbation scenarios. Here, we present a comprehensive benchmark of 27 methods for single-cell perturbation response prediction, evaluated across 29 datasets using 6 complementary performance metrics. By evaluating them under multiple scenarios, we systematically assess their generalizability, including that of emerging foundation models. Our results provide practical guidance for method selection and underscore the need for cellular context embedding approaches to enhance the generalizability of perturbation effect prediction in single-cell research.
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Registered trials
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