An interpretable deep learning framework for biomarker discovery in complex disease survival outcomes

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. We propose SurvDNN, an enhanced deep neural network framework specifically designed for survival outcomes modeling. SurvDNN incorporates a bootstrapping-based regularization strategy to mitigate overfitting and a novel stability-driven filtering algorithm to improve model robustness. To enable interpretable biomarker discovery, we extend the Permutation-based Feature Importance Test (PermFIT) to survival settings.

Recommended citation: Wan S, Mi X, Zou F, Zou B. (2025). "An interpretable deep learning framework for biomarker discovery in complex disease survival outcomes." bioRxiv. 2025.09.30.679415.
Download Paper