Deep learning perturbation models can outperform baselines on calibrated metrics
Nature
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Recent research challenges claims that deep learning models cannot outperform simple baselines in genetic perturbation prediction. By identifying artifacts in common metrics, researchers show that calibrated evaluation methods reveal deep learning models do capture meaningful biological signals beyond naive averages.
