Support Vector Based T-Score for Gene Ranking

Authors:
Piyushkumar A. Mundra;Jagath C. Rajapakse
Affiliations:
Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore and Singapore-MIT Alliance, Singapore and Department of Biological Engineering, Massachu ...
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

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Abstract

T-score between classes and gene expressions is widely used for gene ranking in microarray gene expression data analysis. We propose to use only support vector points for computation of t-scores for gene ranking. The proposed method uses backward elimination of features, similar to Support Vector Machine Recursive Feature Elimination (SVM-RFE) formulation, but achieves better results than SVM-RFE and t-score based feature selection on three benchmark cancer datasets.