An Interpretable Deep Learning Framework for Biomarker Discovery in Complex Disease Survival Outcomes.

ABSTRACT: Identification of important biomarkers associated with complex disease survival outcomes is fundamental for gaining an in-depth understanding of disease mechanisms and advancing precision medicine in conditions such as cancer and cardiovascular disorders. However, these tasks are complicated by the unique nature of time-to-event data, which captures both the occurrence and timing of clinical events. Notably, complex associations such as the non-linear and non-additive biomarker interactions and the high-dimensionality challenge conventional survival data modeling approaches. To address these difficulties, we propose SurvDNN, an enhanced deep neural network framework specifically designed for survival outcomes modeling. SurvDNN incorporates a bootstrapping-based regularization strategy to m

SUBMITTER: Wan S 

PROVIDER: S-EPMC12621730 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature