Enhancing miRNA-Disease Prediction with Attention-Based Multi-View Contrastive Learning

MicroRNAs (miRNAs) play a critical role in gene expression regulation, and their dysregulation is implicated in diseases such as cancer and cardiovascular disorders. Identifying miRNA-disease associations is therefore crucial for advancing prevention and treatment strategies for these conditions. We introduce MCLAMDA (Multi-view Contrastive Learning with Attention Mechanism for miRNA-Disease Association Prediction), a novel method inspired by contrastive learning and attention mechanisms. MCLAMDA constructs diverse similarity networks for miRNAs and diseases and employs both topological and feature contrastive learning to capture comprehensive node information. An attention mechanism further enhances the model by weighting and aggregating node features based on the relative importance of their neighboring nodes. Our approach outperforms five state-of-the-art methods in predictive accuracy. Case studies provide additional support for MCLAMDA’s robustness and reliable predictive performance. The code and data for MCLAMDA are available at https://github.com/Ouyang-cmd/MCLAMDA.