Prediction of potential miRNA-disease associations using matrix decomposition and label propagation

Abstract

Prediction of unobserved microRNA (miRNA)-disease associations is one of the most important research fields due to miRNA’s roles of diagnostic biomarkers and therapeutic targets for large number of human complex diseases. Thus, the development of effective computational methods for identification of novel miRNA-disease associations would provide a unique opportunity to design better therapeutic interventions. In this study, we presented a novel computational model named Matrix Decomposition and Label Propagation for MiRNA-Disease Association prediction (MDLPMDA) by integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. Based on the new adjacency matrix of miRNA-disease associations obtained from matrix decomposition through sparse learning method, the model is presented by implementing label propagation process on the constructed integrated miRNA similarity network and integrated disease similarity network, respectively, and then using an average ensemble strategy to combine the two different prediction models. At last, AUCs of 0.9222 and 0.8490 in global and local leave-one-out cross-validation (LOOCV) proved the model’s reliable performance. In addition, AUC of 0.9211+/-0.0004 in 5-fold cross-validation confirmed its accuracy and stability. We further implemented case studies to predict potential miRNAs associated with human complex diseases based on different versions of HMDD database. We also carried out case studies on diseases without any known related miRNAs to examine the prediction performance of MDLPMDA. At last, the analysis of the assessment results of cross validations and case studies indicated that MDLPMDA could be an effective method to infer novel miRNA-disease associations.