SPYSMDA: SPY Strategy-Based MiRNA-Disease Association Prediction

Abstract

Developing computational models to identify potential miRNA-disease associations in large scale, which could provide better understanding of disease pathology and further boost disease diagnostic and prognostic, has attracted more and more attention. Considering various disadvantages of previous computational models, we proposed the model of SPY Strategy-based MiRNA-Disease Association (SPYSMDA) prediction to infer potential miRNA-disease associations by integrating known miRNA-disease associations, disease semantic similarity network and miRNA functional similarity network. Due to the large amount of ‘missing’ associations in the unlabeled miRNA-disease pairs, simply regarding unlabeled instances as negative training samples would lead to high false negative rates of predicted associations. In this paper, we introduced the concept of ‘spy instances’ to identify reliable negatives for model performance improvement. As a result, SPYSMDA achieved excellent AUCs of 0.8827, 0.8416, and 0.8802 in global leave-one-out cross validation, local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, Esophageal Neoplasms was taken as a case study, where 47 out of top 50 predicted miRNAs were successfully confirmed by recent biological experimental literatures.