Deep learning perturbation models outperform baselines under calibrated metrics, study finds
A study published in Nature Machine Learning introduces a calibration framework showing that deep learning models for genetic perturbation prediction can outperform uninformative baselines when evaluated with well-calibrated metrics. Researchers analyzed 14 datasets and 18 metrics, finding that common benchmarks like mean squared error (MSE) and Pearson correlation of control-referenced deltas…
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Key points
- Study introduces dynamic range fraction (DRF) to calibrate 18 metrics across 14 genetic perturbation datasets
- Weighted metrics (WMSE, weighted R²Δ) and NIR show consistently higher calibration than MSE and Pearson Δ
- Nine deep learning models outperform uninformative baselines under well-calibrated metrics on unseen perturbation and combination tasks
Read the original at Nature Machine Learning primary sourceOpen source ↗
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