Designing interpretable deep learning applications for functional genomics: a quantitative analysis.

ABSTRACT: Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex interactions between and within omics data; however, interpreting and explaining these models can be challenging. Interpretability is essential not only to help progress our understanding of the biological mechanisms underlying traits and diseases but also for establishing trust in these model's efficacy for healthcare applications. Recognizing this importance, recent years have seen the development of numerous diverse interpretability strategies, making it increasingly difficult to navigate the field. In this review, we present a quantitative analysis of the challenges arising when designing interpretable deep learning solutions in function

SUBMITTER: van Hilten A 

PROVIDER: S-EPMC11410376 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature