A signal-to-noise classification model for identification of differentially expressed genes from gene expression data

A major focus in cancer research is identifying genetic markers or biomarkers. To build a robust classifier we have to find out the differentially expressed genes (key genes) in binary classification. The differentially expressed genes or biomarker gene selection is the preprocessing task for cancer classification. In this paper, we have compared the results of two approaches for selecting biomarkers from Leukemia data set. The first approach for feature selection is by implementing k-means clustering and signal-to-noise ratio (SNR) method for gene ranking, the top scored genes from each cluster is selected and given to the classifiers. The second approach uses signal to noise ratio ranking only for feature selection. For validation of both the approaches, we have used k nearest neighbor (kNN), support vector machine (SVM), probabilistic Neural Network (PNN) and Feed Forward Neural Network (fNN). After comparing the final results of two approaches we have got 100%, 96%and 96% accuracy with SVM, kNN and PNN respectively in first approach with five numbers of genes. Whereas, performance of FNN is 2.17 with 10 numbers of genes. In second approach we have got 96%, 96% and 62% accuracies for SVM, kNN and PNN respectively for 5 numbers of genes and the performance of FNN is 2.52 for 10 genes.