MicroRNA target gene prediction of ischemic stroke by using variational Bayesian inference for Gauss mixture model

Abstract

MicroRNAs (miRNAs) as biomarkers of numerous diseases, are a novel group of single-stranded, non-coding small RNA molecules, which can regulate the gene expression and transcription or translation of target genes. Therefore, accurately identifying miRNAs and predicting their potential target genes correlated with ischemic stroke contribute to quick understanding and diagnosis of the pathogenesis of ischemic stroke. In order to identify the targets of miRNAs, the differential expression and expression profiling of mRNAs in genome are integrated by using the Gene Expression Omnibus (GEO) database and limma package. Furthermore, the probabilistic scoring approach called TargetScore, is proposed as a promising new technique combined with the expression and sequence information of the known genes. In this study, the priori and posterior probabilities of target genes were obtained by Variational Bayesian-Gaussian Mixture Model (VB-GMM). Consequently, the target genes of miR-124, miR-221 and miR-223, correlated with ischemic stroke, were predicted using the new target prediction algorithm. Ultimately, the comparable downregulation target genes were obtained by integrating the transcendental and posterior values.