Identifying Co-expressed miRNAs using Multiobjective Optimization

Abstract

The micro RNAs or miRNAs are short non-coding RNAs, which are capable in regulating gene expression in post-transcriptional level. A huge volume of data is generated by expression profiling of miRNAs. From various studies it has been proved that a large proportion of miRNAs tend to form clusters on chromosome. So, in this article we are proposing a multi-objective optimization based clustering algorithm for extraction of relevant information from expression data of miRNA. The proposed method integrates the ability of point symmetry based distance and existing Multi-objective optimization based clustering technique-AMOSA to identify co-regulated or co-expressed miRNA clusters. The superiority of our proposed approach by comparing it with other state-of-the-art clustering methods, is demonstrated on two publicly available miRNA expression data sets using Davies-Bouldin index - an external cluster validity index.