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.