A novel approach for selecting informative genes from gene expression data using Signal-to-Noise Ratio and t-statistics

Abstract

Signal-to-Noise Ratio (SNR) and t-statistics are widely used for gene ranking in the analysis of microarray gene expression data. By implementing these filtering techniques directly to the microarray data may give redundant features, as we may have redundant expression values of number of genes in the data set. By grouping the genes bearing similar expression values in a single cluster and then implementing the given filtering techniques to rank the genes in each cluster and by selecting top ranked genes from each cluster give better result towards biomarker selection. In this paper we have taken four cancer data sets and k-means clustering technique to cluster the genes. Support vector machine and k-nearest Neighbor are used for classification and the method for validation is 10 fold cross validation.