Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.

Abstract

Identifying the cause-and-effect mechanism behind the drug-disease associations is a challenging task. Recent studies indicate that microRNAs (miRNAs) play critical roles in human diseases. Targeting specific miRNAs with drugs to treat diseases provides a new aspect for drug repositioning. Drug repositioning provides a way to identify new clinical applications for approved drugs. Drug discovery is expensive and complicated. Therefore, computational methods are necessary for predicting the potential associations between drugs and diseases based on the target miRNAs. Our approach bilateral-inductive matrix completion (BIMC) performed two rounds of inductive matrix completion algorithm, one on the drug-miRNA and another on the miRNA-disease, association matrices, and integrated the results for predicting the drug-disease relationships through the target miRNAs. The fundamental idea of inductive matrix completion (IMC) is to fill the unknown entries of the association matrices by utilizing existing associations and side information. In our study, the integrated similarities of drugs, miRNAs, and diseases were utilized as side information. Our method predicts drug-miRNA and miRNA-disease associations, as intermediate results. To estimate the performance of our approach, we conducted leave-one-out cross-validation (LOOCV) experiments. The method could achieve AUC scores of 0.792, 0.759, and 0.791 in drug-disease, drug-miRNA, and miRNA-diseases association predictions. The results and case studies indicate the prediction ability of our method, and it is superior to previous models with high robustness. The proposed approach predicts new drug-disease relationships and the causal miRNAs. The top predicted relationships are the promising candidates, and they are released for further biological tests.