Abstrait

A Modified Rough Fuzzy ?Clustering - Classification? Model For Gene Expression Data

Lt.Thomas Scaria, Dr.T Christopher, Gifty Stephen

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