Microarray gene expression ranking with Z-score for Cancer Classification

Abstract

Over the past few decade there has been explosion in the amount of genomic data available to biomedical engineer due to the advantage of biotechnology. For example using microarray it is possible to find out a persons gene expressions profile more than 30000 genomes. Among this one of the most important gene selection problem is gene ranking. Here we will describe Z-score ranking for microarray gene expression selection. In that technique it choose the gene and then applied the Z-Score Ranking technique and then divides the genes into subsets with Successive Feature selection and then finally LDA Applied for the result. With this Z-score ranking technique we will get the accurate results and less effort. The Lymphoma and Leukemia dataset genes are utilized. The proposed technique shows capable classification accuracy for the whole test data sets.