ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol.2, Special Issue 5, October 2014 Copyright to IJIRCCE www.ijircce.com 338 A Modified Rough Fuzzy “Clustering - Classification” Model For Gene Expression Data Lt.Thomas Scaria 1 , Dr.T Christopher 2 , Gifty Stephen 3 Research Scholar, Periyar University, Salem, Tamil Nadu, India 1 Assistant Professor and Head, Department of CS, Govt Arts College, Udumalpet,Tamilnadu, India 2 Assistant Professor, Department of CS, Sir Sayd Institute of Technical Studies, Thaliparamba, Kerala, India 3 ABSTRACT: Microarray technology is one of the important biotechnological means that has made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and a cross collections of related samples . An important application of microarray data is to elucidate the patterns hidden in gene expression data for an enhanced understanding of functional genomics. A microarray gene expression data set can be represented by an expression table, where each row corresponds to one particular gene, each column to a sample or time point, and each entry of the matrix is the measured expression level of a particular gene in a sample or time point, respectively. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in pattern recognition process to reveal natural structures. Recent decades, more and more researchers study on gene expression profile analysis which provides a more precise and reliable way for disease diagnosis and treatment when compared with traditional cancer diagnosis approaches based on the morphological appearance of cells.Through this research we mainly aim toStudy and analyse different clustering and classification model regarding gene expression data, Design and develop an efficient method for gene expression data clustering and classification finally Conduct experimental analysis to evaluate the proposed methodology to prove the significance of the method KEYWORDS: Micro Array, Expression Table, Marker genes, IBSA, Genomics, Proteomics, mRNA I. INTRODUCTION A microarray gene expression data set can be represented by an expression table, where each row corresponds to one particular gene, each column to a sample, and each entry of the matrix is the measured expression level of a particular gene in a sample, respectively [1], [2]. However, for most gene expression data, the number of training samples is still very small compared to the large number of genes involved in the experiments. When the number of genes is significantly greater than the number of samples, it is possible to find biologically relevant correlations of gene behaviour with the sample categories or response variables [3]. However, among the large amount of genes, only a small fraction is effective for performing a certain task. Also a small subset of genes is desirable in developing gene expression-based diagnostic tools for delivering precise, reliable, and interpretable results [4]. With the gene selection results, the cost of biological experiment and decision can be greatly reduced by analysing only the marker genes. Hence, identifying a reduced set of most relevant genes is the goal of gene selection. Cluster analysis is a technique for finding natural groups present in the gene set. It divides a given gene set into a set of clusters in such a way that two genes from the same cluster are as similar as possible and the genes from different clusters are as dissimilar as possible [7], [8]. To understand gene function, gene regulation, cellular processes, and subtypes of cells, clustering techniques have proven to be helpful. The co-expressed genes, that is, genes with similar expression patterns, can be clustered together with similar cellular