Analysis of Population Based Metaheuristic Used for Gene Clustering

Abstract

Gene Clustering is one of the most popular application in the field of Bioinformatics. It is a method of grouping gene into clusters, such that each cluster must have similar gene expression levels. The two most popular population based globalized search algorithms Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are used for clustering gene expression data but the main drawback of these two algorithm's is that they got trapped in local optima while exploring across the problem space. So the PSO and GA has been hybridized with several other methods, to overcome the local optima problem. This paper presents a literature survey of both GA and PSO's application in Gene Clustering. PSO and GA variants and their hybridization with other metaheuristics are also described in this paper. An attempt is made to provide a guide for the researchers who are working in the area of population based gene clustering. Bioinformatics field deals with the analysis of the biological data, which stored information in the form of DNA, protein sequence in various biological databases. The Bioinformatics field involves various problem such as Gene Clustering Problem, Molecular Docking Problem, Multiple Sequence Alignment Problems, Phylogenetic tree construction , RNA Secondary Structure Prediction, Protein Secondary Structure Prediction, Fragment Assembly Problem. In Gene Clustering the gene is defined as a sequence of nucleotide bases, which carry the required information for the purpose of protein formation, which helps in the construction of the structural components. Clustering is a process of combining input patterns into groups based on the similar functionality. So, Gene clustering is a process of grouping genes in the same clusters based on similar gene expression levels. Gene expression refers to a process through which the coded information from a gene is converted into structures operating in the cell. In order to cluster the Gene expression data , various population based algorithms were used by a number of researchers. The two most popular algorithms among these are PSO (Particle Swarm Optimization) and