Self-Improving, Interpretable AI for Decoding Gene Regulation

This Nature Genetics review surveys how sequence-to-function models link DNA sequence to regulatory readouts, analyzes why strong predictive accuracy often fails to yield robust mechanistic understanding across genetic variation and cell contexts, and argues for continual AI–experiment feedback loops, targeted perturbations, and systematic evaluation to progressively deepen mechanistic insight and improve biological discovery.