Predicting miRNA–Disease Associations Based on Contrastive Learning of Linear and Nonlinear Features

Background: MicroRNAs (miRNAs) regulate biological processes and are associated with human diseases. Computational prediction can prioritize candidate associations for experimental investigation, but many existing methods emphasize nonlinear representations and may underuse complementary linear information and interactions between miRNA and disease features. This study aimed to integrate these complementary representations for miRNA–disease association prediction. Methods: We developed Singular Value Decomposition and Graph Autoencoder for miRNA–Disease Association prediction (SGMDA). Singular value decomposition extracted linear representations from the association matrix, while graph autoencoders learned nonlinear representations from integrated miRNA and disease similarity graphs. Contrastive learning aligned the two representation spaces, and a highway network modeled interactions between the fused miRNA and disease features. A random forest classifier generated association scores. Evaluation included pair-wise five-fold cross-validation, global leave-one-out cross-validation, miRNA-wise and disease-wise cross-validation, ablation experiments, and case studies of lung and prostate neoplasms. Results: The reported comparisons showed competitive predictive performance across the evaluated settings, with lower performance under entity-wise cross-validation than under pair-wise cross-validation. Ablation experiments supported the complementary contributions of linear and nonlinear features, contrastive alignment, and highway-based feature interactions. Many highly ranked case-study candidates were supported by at least one external database. Temporal validation using different HMDD releases provided additional retrospective evidence for prioritizing subsequently recorded associations. Conclusions: Combining linear and nonlinear representations with contrastive alignment and feature-interaction modeling provides a useful framework for prioritizing potential miRNA–disease associations. Database support does not establish biological causality or constitute strict independent validation, and the predicted associations require further experimental investigation.