Identifying mammalian MicroRNA targets based on supervised distance metric learning.

Abstract

MicroRNAs (miRNAs) have been emerged as a novel class of endogenous post-transcriptional regulators in a variety of animal and plant species. One challenge facing miRNA research is to accurately identify the target mRNAs, because of the very limited sequence complementarity between miRNAs and their target sites, and the scarcity of experimentally validated targets to guide accurate prediction. In this paper, we propose a new method called SuperMirTar that exploits supervised distance learning to predict miRNA targets. Specifically, we use the experimentally supported miRNA-mRNA pairs as training set to learn a distance metric function that minimizes the distances between miRNAs and mRNAs with validated interactions, then use the learned function to calculate the distances of test miRNAmRNA interactions, and those with smaller distances than a predefined threshold are regarded as true interactions. We carry out performance comparison between the proposed approach and seven existing methods on independent datasets, the results show that our method achieves superior performance and can effectively narrow the gap between the number of predicted miRNA targets and the number of experimentally validated ones.