MPHGNN: metapath-guided heterogeneous graph neural network for miRNA-drug resistance association prediction.

ABSTRACT: Aberrant expression of microRNAs (miRNAs) is closely associated with the pathogenesis and progression of various diseases, particularly cancer, as well as therapeutic responses. Identification of miRNA-drug resistance associations is critical for drug screening and precision medicine. However, conventional experimental approaches remain time-consuming and labor-intensive, while existing computational methods often face challenge in capturing higher-order semantic inference from sparse prior bipartite association network. To address this, we propose MPHGNN, a heterogeneous graph convolutional network (GCN) architecture for predicting miRNA-drug resistance associations. MPHGNN constructs a miRNA-gene-drug heterogeneous network with multimodal biological features, including miRNA expression p

SUBMITTER: Huang G 

PROVIDER: S-EPMC12853127 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature