ArticleNature computational science2025
In silico biological discovery with large perturbation models.
Article in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- In Vivo Direct Reprogramming: Current Progress and Future Prospects from Mechanisms to Therapeutic Application.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Bringing human early embryo development to clinical interpretation: HESTA as an evolving reference.Clinical and translational medicine · 2026Article
- An Agentic Platform for Drug Repurposing Unified across Molecular, Phenotypic, and Clinical Scales.bioRxiv : the preprint server for biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Keeping generative artificial intelligence reliable in omics biology.Patterns (New York, N.Y.) · 2026Article
- Artificial intelligence as decision support for adolescent depression and anxiety: a mini review of clinical utility, safety, and implementation.Frontiers in psychiatry · 2026Review
- Bidirectional network hubs: NT-genes as candidate targets for partial cancer reversal.Frontiers in systems biology · 2026Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data generated in perturbation experiments link perturbations to the changes they elicit and therefore contain information relevant to numerous biological discovery tasks-from understanding the relationships between biological entities to developing therapeutics. However, these data encompass diverse perturbations and readouts, and the complex dependence of experimental outcomes on their biological context makes it challenging to integrate insights across experiments. Here we present the large perturbation model (LPM), a deep-learning model that integrates multiple, heterogeneous perturbation experiments by representing perturbation, readout and context as disentangled dimensions. LPM outperforms existing methods across multiple biological discovery tasks, including in predicting post-perturbation transcriptomes of unseen experiments, identifying shared molecular mechanisms of action between chemical and genetic perturbations, and facilitating the inference of gene-gene interaction networks. LPM learns meaningful joint representations of perturbations, readouts and contexts, enables the study of biological relationships in silico and could considerably accelerate the derivation of insights from pooled perturbation experiments.
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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.