An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph.

ABSTRACT: Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our

SUBMITTER: He J 

PROVIDER: S-EPMC12446772 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature