ArticleNature methods2025
Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
107 citing papers in PubMed.
- A systematic transcriptomic atlas and predictive framework for metabolite-driven T cell state transitions.iScience · 2026Article
- Deep learning perturbation models can outperform baselines on calibrated metrics.Nature biotechnology · 2026Article
- The next 25 years of cancer research: emerging perspectives and priorities.Nature reviews. Cancer · 2026Article
- Predicting transcriptional regulators in plants in the era of artificial intelligence.Current opinion in plant biology · 2026Review
- Review
- Cell-level random splits leak group-owned answers in single-cell benchmarks.bioRxiv : the preprint server for biology · 2026Article
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- scXpand: Pan-cancer detection of T cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing.Cell genomics · 2026Article
- Article
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- scDiagnostics: systematic assessment of cell type annotation in single-cell transcriptomics data.Briefings in bioinformatics · 2026Article
- CFM-GP: unified conditional flow matching to learn gene perturbation across cell types.NAR genomics and bioinformatics · 2026Article
- A systematic assessment of single-cell language model configurations.NAR genomics and bioinformatics · 2026Article
- Ensuring multiomics data reproducibility for artificial intelligence with reference materials as a common calibrator.Nature biotechnology · 2026Article
- Benchmarking biomedical foundation models.Nature methods · 2026Review
- Induced pluripotent stem cells from discovery to translation.Nature medicine · 2026Review
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- Integration of artificial intelligence and multi-omics for precision medicine.Functional & integrative genomics · 2026Review
- Bridging precision agriculture and human medicine through comparative genetics.Nature reviews. Genetics · 2026Review
- Article
47 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicting transcriptome changes after single or double perturbations. None outperformed the baselines, which highlights the importance of critical benchmarking in directing and evaluating method development.
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What OpenQuestion holds
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.