Deep learning-based ranking method for subgroup and predictive biomarker identification in patients.

ABSTRACT:

Background

The task of identifying patient subgroups with enhanced treatment responses is important for clinical drug development. However, existing deep learning-based approaches often struggle to provide clear biological insights. This study aims to develop a deep learning method that not only captures treatment effect differences among individuals but also helps uncover meaningful biological markers associated with those differences.

Methods

We introduce DeepRAB, a deep learning-based framework designed for exploring treatment effect heterogeneity by constructing individualized treatment rule (ITR). In addition, DeepRAB enables model interpretability by facilitating predictive biomarker identification. We evaluate its performance using simulated datasets that vary in comp

SUBMITTER: Liu Z 

PROVIDER: S-EPMC12152138 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature