Toward generalizable and interpretable AI in regulatory genomics.

Sequence-to-function models in regulatory genomics achieve high accuracy but struggle with generalization across genetic and cellular contexts, highlighting a need for continual refinement.

• Why it matters: Understanding how DNA encodes gene regulation is essential for biological insight and medical applications, yet current models often fail to reliably interpret diverse biological variations.
• What they did: The authors review how model architecture, training data, and evaluation influence model behavior, emphasizing the importance of interpretability methods and systematic testing to identify failure modes.
• The result: They propose a framework where models are iteratively refined through targeted experiments and feedback loops, enabling more robust mechanistic understanding and improved biological discovery.