Deep learning perturbation models can outperform baselines on calibrated metrics
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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Cites
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Flagged first, then by citations
- Authors
- Miller, H. E., Mejia, G. M., Leblanc, F. J. A., Swain, B., Wang, B., & de Lima Camillo, L. P.
- Venue
- Nature Biotechnology
- Published
- 2026-10-01
- ISSN
- 1087-0156
- 1546-1696
- Cited by
- 5
- References
- 11
- Language
- en
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