Simultaneous sample and gene selection using t-score and approximate support vectors

Abstract

T-score, based on t-statistics between samples and disease classes, is a widely used filter criterion for gene selection from microarray data. However, classical T-score uses all the training samples but for both biological and computational reasons, selection of relevant samples for training is an important step in classification. Using a modified logistic regression approach, we propose a sample selection criterion based on T-score and develop a backward elimination approach for gene selection. The method is more stable and computationally less costly compared to support vector machine recursive feature elimination (SVM-RFE) methods. © 2013 Springer-Verlag Berlin Heidelberg.