DHCLHAM: microbe-drug interaction prediction based on dual-hypergraph contrastive learning framework with hierarchical attention mechanism.

ABSTRACT:

Introduction

Various drugs can markedly disrupt gut microbiota, resulting in a reduction of beneficial microbial populations and precipitating a range of negative clinical consequences. Traditional experimental methods have considerable limitations in clarifying the mechanisms of microbe-drug interactions, thereby necessitating the creation of innovative computational techniques to establish theoretical foundations for personalized and precision medicine. However, the majority of current computational methods rely on graph structures, which inadequately represent the intricate, varied, and heterogeneous interactions among multiple drugs and microbial communities.

Methods

We introduce a hierarchical attention-driven dual-hypergraph contrastive learning framework for predictin

SUBMITTER: Hu H 

PROVIDER: S-EPMC12531137 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature