Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines
Nature Methods 22, 1657–1661 (2025, brief communication), published online 4 August 2025. The article page gives Received 11 October 2024, Accepted 24 June 2025, Published 4 August 2025, Issue date August 2025.
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- No authorship claim recorded yet. The source record lists Constantin Ahlmann-Eltze, Wolfgang Huber and Simon Anders.
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- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines Nature Methods · published 2025-08-04 · Retrieved for the candidate pack on 2026-10-30 (SHA-256 3ad786d9b77f25dae848ab4354e643e1327a3ca8b792a4d577caf75654567656). Nature Methods article page, version of record of 4 August 2025, as served on 2026-10-30.
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- AI models can simulate or predict the behaviour of cells (a "virtual cell")Ahlmann-Eltze, Huber and Anders (Nature Methods, published online 4 August 2025) report that none of five foundation models and two other deep-learning models outperformed deliberately simple baselines at predicting transcriptome changes after genetic perturbations, and conclude that the goal of predicting the outcome of not-yet-performed experiments is still elusive.2025-08-04 · publication · Stated in source · Supported Inspect claim & evidence ↗AI models can simulate or predict the behaviour of cells (a "virtual cell"): propositions page →
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