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

Authors:
Piyushkumar A. Mundra;Jagath C. Rajapakse;D. A. K. Maduranga
Affiliations:
Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore,Singapore-MIT Alliance, Singapore,Department of Biological Engineering, Massachusetts In ...;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Year:
2013

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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.