Deep Learning and Explainable AI: New Pathways to Genetic Insights
Journal:
arXiv
Published Date:
May 15, 2025
Abstract
Deep learning-based AI models have been extensively applied in genomics,
achieving remarkable success across diverse applications. As these models gain
prominence, there exists an urgent need for interpretability methods to
establish trustworthiness in model-driven decisions. For genetic researchers,
interpretable insights derived from these models hold significant value in
providing novel perspectives for understanding biological processes. Current
interpretability analyses in genomics predominantly rely on intuition and
experience rather than rigorous theoretical foundations. In this review, we
systematically categorize interpretability methods into input-based and
model-based approaches, while critically evaluating their limitations through
concrete biological application scenarios. Furthermore, we establish
theoretical underpinnings to elucidate the origins of these constraints through
formal mathematical demonstrations, aiming to assist genetic researchers in
better understanding and designing models in the future. Finally, we provide
feasible suggestions for future research on interpretability in the field of
genetics.
