BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.

ABSTRACT: Drug-gene interactions (DGIs) influence the toxicity or ineffectiveness of the drug therapy and play an important role in elucidating drug mechanisms, predicting potential adverse effects, and facilitating precision medicine. Existing computational methods typically rely on chemical or genetic sequence features of drugs and genes, limiting their effectiveness for novel entities lacking explicit annotations. To address this, we propose BiGvCL, a framework that predicts DGIs exclusively based on network topology, requiring no explicit feature information for drugs or genes. BiGvCL introduces a lightweight graph attention mechanism (GATLite) to efficiently aggregate local neighborhood information. Additionally, we develop a gated graph convolutional network (GatedGCN) to explicitly learn high

SUBMITTER: He S 

PROVIDER: S-EPMC12848949 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature