Inferring Disease–miRNA Associations by Self-Weighting with Multiple Data Source

Abstract

Increasing evidence has suggested that microRNAs (miRNAs) may function as positive regulators at the post-transcriptional level. A search for associations between miRNAs and diseases is crucial for understanding the pathogenesis. Various publicly available databases have been constructed to store meaningful information on a large number of miRNA molecules. In this study, to resolve the limitation that individual sources of miRNA target data tend to be incomplete and noisy, we propose a network-based computational method called self-weighting for integrating multiple data sources. A bipartite phenotype-miRNA network (BPMN) incorporates known disease–miRNA interactions as well as the similarities between disease phenotypes and functional similarities of miRNAs. Random walk with restart algorithm was deployed on the bipartite network to predict novel disease–miRNA associations. In leave-one-out cross-validation experiments, our technique achieves an AUC of 0.801 when evaluating against known disease-related miRNAs from HMDD. Systematic prioritization of miRNAs for 11 common diseases obtained an average AUC of 0.765. Additionally, a case study on colon cancer uncovered a number of potential miRNA candidates as biomarkers of this disease.