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 profiles, drug structural descriptors, and gene functional similarities, and leverages dual learning modules at both metapath and global levels to capture localized patterns and global representations simultaneously. Experimental results demonstrate that MPHGNN outperforms state-of-the-art methods and enhances the discriminative ability of association representations. Interpretability analyses further reveal that metapaths effectively capture underlying biological mechanisms, while the constructed heterogeneous biological network makes important contributions to prediction.
Li et al. (Thu,) studied this question.
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