ArticleBioinformatics (Oxford, England)2025
Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- 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
- Benchmarking biomedical foundation models.Nature methods · 2026Review
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- Computational modelling of cell identity.The Biochemical journal · 2026Review
- TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects.Nature biotechnology · 2026Article
- Mapping functional non-coding variation in individual human genomes through haplotyping, multiomics, and deep learning.Nature communications · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- Virtual Cells Need Context, Not Just Scale.bioRxiv : the preprint server for biology · 2026Article
- Data-driven discovery of digital twins in biomedical research.Briefings in bioinformatics · 2026Review
- 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
- SpatialProp: tissue perturbation modeling with spatially resolved single-cell transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- A comparison of computational methods for expression forecasting.Genome biology · 2025Article
- Transformative advances in single-cell omics: a comprehensive review of foundation models, multimodal integration and computational ecosystems.Journal of translational medicine · 2025Review
- Mapping transcriptional responses to cellular perturbation dictionaries with RNA fingerprinting.bioRxiv : the preprint server for biology · 2025Article
- Predicting the unseen: a diffusion-based debiasing framework for transcriptional response prediction at single-cell resolution.bioRxiv : the preprint server for biology · 2025Article
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.Nature methods · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
motivationModeling genetic perturbations and their effect on the transcriptome is a key area of pharmaceutical research. Due to the complexity of the transcriptome, there has been much excitement and development in deep learning (DL) because of its ability to model complex relationships. In particular, the transformer-based foundation model paradigm emerged as the gold-standard of predicting post-perturbation responses. However, understanding these increasingly complex models and evaluating their practical utility is lacking, along with simple but appropriate benchmarks to compare predictive methods.
resultsHere, we present a simple baseline method that outperforms both state of the art (SOTA) in DL and other proposed simpler neural architectures, setting a necessary benchmark to evaluate in the field of post-perturbation prediction. We also elucidate the utility of foundation models for the task of post-perturbation prediction via generalizable fine-tuning experiments that can be translated to different applications of transformer-based foundation models to tasks of interest. Furthermore, we provide a corrected version of a popular dataset used for benchmarking perturbation prediction models. Our hope is that this work will properly contextualize further development of DL models in the perturbation space with necessary control procedures. AVAILABILITY AND IMPLEMENTATION: All source code is available at: https://github.com/pfizer-opensource/perturb_seq. The DOI is 10.5281/zenodo.15352937.
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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.