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Protein-affinity benchmark finds deep-learning models vary across targets

A bioRxiv preprint introduces PLABench, a benchmark comparing nine deep-learning methods for predicting protein–ligand binding affinity. Its authors report that strong overall accuracy did not translate into consistent performance across targets or protein families.

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Randomly split single-cell data can inflate machine-learning scores

A bioRxiv preprint finds that randomly dividing single-cell data can leak information between training and test sets when related cells share labels. The authors propose eakcheck, a tool that estimates this leakage before a predictive model is trained.