Multi-view Graph Attention Contrastive Learning for Predicting miRNA-Disease Association

MicroRNAs miRNAs are an important class of endogenous non-coding RNAs that regulate critical biological processes, such as cell differentiation, proliferation, and apoptosis, through post-transcriptional mechanisms. Recent studies have shown that aberrant miRNA expression is closely linked to the pathogenesis of complex diseases, including cancer and neurodegenerative disorders. This study introduces a novel prediction model, MGACMDA, which combines multi-view contrastive learning and residual graph attention to overcome several limitations of existing miRNA-disease association prediction methods, such as limited robustness to data sparsity, high sensitivity to network noise, and insufficient extraction of deep topological information. We propose three data enhancement methods to construct global, local and topological views, and design a graph attention encoder with residual connection to fuse shallow topological features with deep representations through residual mechanism. Finally, a momentum-driven multi-view contrastive learning module is designed, and momentum encoder is used to maintain the global negative sample queue, which significantly improves the discrimination ability of sparse association. We applied MGACMDA to benchmark datasets, including HMDD v2.0 and HMDD v3.2, using 5-fold cross-validation. The evaluation metrics, including F1 score, AUC and AUPR values, and case studies of experimental results indicate that our method is efficient and robust for predicting miRNA-disease associations.