Deep learning can capture subtle sequence patterns, but only when the data are curated and balanced. In genomics, mislabeled examples or uneven class representation can skew performance quickly.

Interpretability is essential. I prioritize model designs that allow feature attribution or motif discovery so that biological insight accompanies predictive accuracy.

Model evaluation should reflect real-world data. Cross-species validation and temporal splits reveal whether a model generalizes beyond the training dataset.