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Researchers tackle batch effects, the technical differences that can distort biological datasets, with two approaches designed for labeled and unlabeled cell data.

A physics-inspired training method improves predictions of how mutations affect proteins, revealing why the best learning settings depend on the task.

A physics-informed machine-learning framework predicts how mutations alter protein–RNA interactions, potentially giving researchers a sharper tool for studying molecular variation.