Simultaneous Clustering and Feature Weighting Using Multiobjective Optimization for Identifying Functionally Similar miRNAs.

Abstract

MicroRNAs (miRNAs) are a type of RNAs, which are responsible for monitoring the gene expression values. Recent research asserts that miRNAs form some clustering on chromosomes. The miRNAs belonging to a particular cluster are highly similar in terms of their activity and they are termed as "coregulated" miRNAs. The current paper presents an approach that simultaneously performs two tasks: i) clustering of miRNAs into different categories based on some similarity measures ii) identification of proper weight values for different time points with respect to which expression values are available. In general, a large number of expression values are available for a given miRNA data set. All these values may not be suitable to be used equally to measure the similarity between two miRNAs. In the current study, the problem of proper selection of weight values for different time points and then determining the proper partitioning from the given miRNA data set utilizing the similarity computed using the new set of weight values is formulated as an optimization problem where several cluster validity indices are optimized as the goodness measures. To that end, a multiobjective differential evolution based optimization technique is utilized. The supremacy of the proposed technique is tested on three miRNA data sets in comparison to some recent approaches in terms of some popular performance measures like Silhouette index and DB-index. The observations are further supported by statistical and biological significance tests. Supplementary information is available at https://www.iitp.ac.in/~sriparna/journals.html.