MHMDA: “Similarity-Association-Similarity” Metapaths and Heterogeneous-Hyper Network Learning for MiRNA-Disease Association Prediction

In recent years, microRNA (miRNA) has been recognized as crucial in the progression of human diseases. However, existing computational methods for identifying miRNAdisease associations often overlook the rich association information contained in specific long-distance pathways and lack effective exploration of potential associations. In this study, we propose a biologically interpretable "similarity-associationsimilarity" metapath and heterogeneous-hyper network (HeteroHyperNet) learning approach for miRNA-disease association prediction (MHMDA). In MHMDA, a "similarity-associationsimilarity" multi-hop metapaths learning method based on hierarchical attention perception is proposed to explore specific long-distance associated pathway information connecting potentially associated miRNAs and diseases. In addition, a HeteroHyperNet learning approach integrating heterogeneous network and hyper network is designed to progressively learn direct association information and potential association information between miRNA and disease. The "similarity-associationsimilarity" metapath with hierarchical attention significantly enhances the learning of long-distance biological associations, while the HeteroHyperNet comprehensively learns the known and potential associations of miRNA-disease, greatly improving the richness and accuracy of information. A large number of experimental results show that MHMDA has demonstrated excellent performance in the prediction of miRNA-disease association. In addition, cross independent dataset experiment and cold start experiment on miRNA and disease prove the effectiveness of MHMDA on sparse association points, and its stability and reliability in predicting potential miRNA-disease association are further confirmed. The data and source codes are available at https://github.com/ningq669/MHMDA/.