Prediction of miRNA-Disease Association Based on Biodiversity Association Network

MiRNA-disease association identification is of great significance to the development of clinical medicine and drug research. Present computational methods didn't consider rich biological information, such as the expression level changes of disease-related miRNAs and the association information between miRNA, disease and other types of biological entities. In this study, we propose a new method for prediction of MiRNA-Disease Association based on Biodiversity Association Network (BANMDA). BANMDA first collects multiple types of association information from multiple sources, including diseases, miRNAs and lncRNAs associations, expression level changes of disease-related miRNAs, miRNAs sequence information, and disease semantic information. Second, BANMDA extracts diversity association features and diversity biological features based on two heterogeneous graph structure to represent miRNAs and diseases at multiple levels. In diversity association module, edges are classified according to the expression level changes of disease-related miRNAs and the similarities between miRNAs and diseases. In diversity node module, lncRNAs associated with miRNA-diseases are collected to construct heterogeneous network and we propose an improved GCN algorithm to directly aggregate the higher-order neighborhood information in the heterogeneous graph. Finally, the bilinear decoder is applied to predict associations between miRNAs and diseases. Experimental results show that BANMDA can be used as a powerful tool to identify miRNA-disease associations.