An Archived Multi Objective Simulated Annealing Method to Discover Biclusters in Microarray Data

Abstract

With the advent of microarray technology it has been possible to measure thousands of expression values of genes in a single experiment. Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering methods. Recently, biclustering techniques were proposed for revealing submatrices showing unique patterns. Biclustering or simultaneous clustering of both genes and conditions is challenging particularly for the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. In biclustering of microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A multi objective model is very suitable for solving this problem. Our method proposes a algorithm which is based on multi objective Simulated Annealing for discovering biclusters in gene expression data. Experimental result in bench mark data base present a significant improvement in overlap among biclusters and coverage of elements in gene expression and quality of biclusters.