Predicting miRNA-disease associations via multi-channel graph convolutional networks

Extensive research evidence shows that variation and dysregulation of microRNAs(miRNAs) are important causes of disease, and therefore the study of miRNA-disease associations has important theoretical and applied implications in the field of human disease research and treatment. Based on the time and cost of validating miRNA-disease associations in traditional medicine clinical experiments, using multiple biological datasets to predict potential miRNA-disease associations (MDAs) has become a hot topic in the field of biological research in recent years. This paper develops a novel model of MDA-RGCN based on a multi-channel graph convolutional network and graph attention for MDAs prediction. Based on graph theory, this study treats MDAs prediction as a node classification task. To learn the topology and various interactions between feature graph nodes of various strengths, we employ two independent graph attention networks, which increases training efficiency and accuracy. In order to learn information that is shared by both graphs, we employ a GCN with a shared weight matrix simultaneously. Comprehensive experiments reveal that the prediction performance of MDA-RGCN excels other more sophisticated models for MDAs prediction. Furthermore, we further confirmed the predictive ability of MDA-RGCN to identify potential disease-related miRNAs by selecting two human diseases for case study.