Nature Genetics Review: Why High Accuracy Fails to Generalize in AI Genomics Models
anshulkundaje · x · 2026-08-07
A comprehensive review in Nature Genetics examines the current landscape and challenges of applying AI to regulatory genomics.
The authors highlight that while sequence-to-function (seq2func) models excel at predicting molecular regulatory readouts and variant effects from DNA sequences, their generalizability across genetic variations and cellular contexts remains highly inconsistent.
The paper synthesizes how model architectures, training data, and prediction tasks shape model behavior. It also reveals systematic failure modes in current interpretability practices, explaining why strong predictive accuracy often fails to translate into a robust understanding of regulatory mechanisms. The review argues that future progress requires reframing evaluation frameworks to prioritize genuine mechanistic understanding over raw predictive metrics.
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