Genes
Predicting miRNA-Disease Associations Based on Contrastive Learning of Linear and Nonlinear Features
By Sun Y, Zhu W and Li L.
Abstract
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.