Perturbation response prediction
Also known as: perturbation prediction · perturbation effect prediction
Predicting transcriptional responses to genetic or drug perturbations, as done by GEARS, CPA and State. Independent benchmarks report that deep-learning models do not yet reliably outperform simple linear baselines. VC0013 VC0014 VC0019 VC0048 VC0049
Review pending Version 0.1.0 · status: draft · last expert review: not yet reviewed
Relations
| Subject | Relation | Object | Confidence | Evidence (role) |
|---|---|---|---|---|
| Perturbation response prediction | benchmarked on | Simple linear baseline | high | VC0048 benchmark VC0049 benchmark Deep models do not yet reliably outperform simple linear baselines. |
| GEARS | associated with | Perturbation response prediction | high | VC0013 supports From: AI virtual cell models |
| CPA | associated with | Perturbation response prediction | high | VC0014 supports From: AI virtual cell models |
| State | associated with | Perturbation response prediction | high | VC0019 supports From: AI virtual cell models |
| Systematic variation | associated with | Perturbation response prediction | high | VC0052 benchmark Systematic variation can lead to overestimated predictor performance. |
| X-Cell | associated with | Perturbation response prediction | medium | VC0033 · preprint supports Preprint. From: AI virtual cell models |
Appears in research topics
- AI virtual cell models (Models)
- Validating virtual cells: benchmarks, baselines and critiques (Validation)
Appears in library items (15)
Items whose title, tags or one-line summary match this entity (derived from the data), plus the items cited as evidence for the description.
- VC0019 Predicting cellular responses to perturbation across diverse contexts with State. — Adduri AK et al., Cell, 2026
- VC0138 · preprint PerturbLDM: conditional latent diffusion for modelling single-cell perturbation responses — Yu et al., bioRxiv, 2026
- VC0054 · preprint An Empirical Comparison of Virtual Cell Models: Perturbation Prediction, Representation, and the Baseline Gap — Denvis O., Research Square, 2026
- VC0018 TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects. — Wenkel F et al., Nature biotechnology, 2026
- VC0033 · preprint X-Cell: Scaling Causal Perturbation Prediction Across Diverse Cellular Contexts via Diffusion Language Models — Wang C et al., bioRxiv, 2026
- VC0129 · preprint DELPHAI, AI Agent for Predicting Drug Response and Resistance — Peng et al., preprint, 2025
- VC0052 Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. — Viñas Torné R, Wiatrak M, Piran Z, Fan S, Jiang L, Teichmann SA, Nitzan M, Brbić M., Nature biotechnology, 2025
- VC0048 Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. — Ahlmann-Eltze C, Huber W, Anders S., Nature methods, 2025
- VC0055 · preprint Multimodal Benchmarking of Foundation Model Representations for Cellular Perturbation Response Prediction — Hasanaj E, Cole E, Mohammadi S, Addagudi S, Zhang X, Song L, Xing EP., bioRxiv, 2025
- VC0049 Benchmarking foundation cell models for post-perturbation RNA-seq prediction. — Csendes G, Sanz G, Szalay KZ, Szalai B., BMC genomics, 2025
- VC0051 · preprint PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction — Wenteler A, Occhetta M, Branson N, Curean V, Huebner M, Dee W, Connell W, Chung SP, Hawkins-Hooker A, Ektefaie Y, Valdez Córdova CM, Gallagher-Syed A., bioRxiv, 2024
- VC0016 Learning single-cell perturbation responses using neural optimal transport. — Bunne C, Stark SG, Gut G, Del Castillo JS, Levesque M, Lehmann KV, Pelkmans L, Krause A, Rätsch G., Nature methods, 2023
- VC0013 Predicting transcriptional outcomes of novel multigene perturbations with GEARS. — Roohani Y, Huang K, Leskovec J., Nature biotechnology, 2023
- VC0014 Predicting cellular responses to complex perturbations in high-throughput screens. — Lotfollahi M et al., Molecular systems biology, 2023
- VC0015 scGen predicts single-cell perturbation responses. — Lotfollahi M, Wolf FA, Theis FJ., Nature methods, 2019
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