ArticleNature biotechnology2026
Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Deep learning perturbation models can outperform baselines on calibrated metrics.Nature biotechnology · 2026Article
- Review
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Toward generalizable and interpretable AI in regulatory genomics.Nature genetics · 2026Review
- Analytical Methods and Application of Single-Cell and Single-Nucleus Transcriptomics in the Study of Ischemic Stroke.Biomolecules · 2026Review
- Evaluating the learnability of single-cell large language models on multiple tasks.BMC genomics · 2026Article
- A community machine learning challenge to predict the effects of gene perturbations on T cell differentiation for cancer immunotherapy.bioRxiv : the preprint server for biology · 2026Article
- scArchon: a scalable benchmarking framework for assessing single-cell perturbation models.Genome biology · 2026Article
- Spurious correlation inflates performance in single-cell perturbation prediction.bioRxiv : the preprint server for biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 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
- Article
- Predicting the unseen: A diffusion-based debiasing framework for transcriptional response prediction at single-cell resolution.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Predicting transcriptional responses to genetic perturbations is challenging in functional genomics. While recent methods aim to infer effects of untested perturbations, their true predictive power remains unclear. Here, we show that current methods struggle to generalize beyond systematic variation, the consistent transcriptional differences between perturbed and control cells arising from selection biases or confounders. We quantify this variation in ten datasets, spanning three technologies and five cell lines, and show that common metrics are susceptible to these biases, leading to overestimated performance. To address this, we introduce Systema, an evaluation framework that emphasizes perturbation-specific effects and identifies predictions that correctly reconstruct the perturbation landscape. Using this framework, we uncover insights into the predictive capabilities of existing methods and show that predicting responses to unseen perturbations is substantially harder than standard metrics suggest. Our work highlights the importance of heterogeneous gene panels and disentangles predictive performance from systematic effects, enabling biologically meaningful developments in perturbation response modeling.
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Registered trials
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.