ArticleNature biotechnology2026
Deep learning perturbation models can outperform baselines on calibrated metrics.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Recent benchmarks report that deep-learning-based genetic perturbation models fail to outperform uninformative baselines. Here we introduce a positive control baseline and metric calibration framework, showing-across 14 datasets and 18 metrics-that common benchmarking metrics (for example, mean squared error and Pearson Δ) are frequently miscalibrated, with reduced sensitivity to measure positive model performance. Under well-calibrated metrics, we find that deep-learning-based genetic perturbation models can outperform uninformative baselines.
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42823510What OpenQuestion holds
Registered trials
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