A Computational Model to Predict the Causal miRNAs for Diseases

Abstract

MicroRNAs (miRNAs) are one class of important noncoding RNA molecules, and their dysfunction is associated with a number of diseases. Currently, a series of databases and algorithms have been developed for dissecting human miRNA-disease associations. However, these tools only presented the associations between miRNAs and disease but did not address whether the associations are causal or not, a key biomedical issue that is critical for understanding the roles of candidate miRNAs in the mechanisms of specific diseases. Here we first manually curated causal miRNA-disease association information and updated the human miRNA disease database (HMDD) accordingly. Then we built a computational model, MDCAP (MiRNA-Disease Causal Association Predictor), to predict novel causal miRNA-disease associations. As a result, we collected 6,667 causal miRNA-disease associations between 616 miRNAs and 440 diseases, which accounts for ∼20% of the total data in HMDD. The MDCAP model achieved an area under the receiver operating characteristic (ROC) curve of 0.928 for ROC analysis by independent test and an area under the ROC curve of 0.925 for ROC analysis by 10-fold cross-validation. Finally, case studies conducted on myocardial infarction and hsa-mir-498 further suggested the biomedical significance of the predictions.

Keywords: disease; miRNA functional similarity; miRNAs; miRNA–disease association prediction; network biology.