Title: SVM-RFE based feature selection for tandem mass spectrum quality assessment

Authors: Jiarui Ding, Jinhong Shi, Fang-Xiang Wu

Addresses: Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada. ' Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada. ' Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada

Abstract: In literature, hundreds of features have been proposed to assess the quality of tandem mass spectra. However, many of these features are irrelevant in describing the spectrum quality and they can degenerate the spectrum quality assessment performance. We propose a two-stage Recursive Feature Elimination based on Support Vector Machine (SVM-RFE) method to select the highly relevant features from those collected in literature. Classifiers are trained to verify the relevance of selected features. The results demonstrate that these selected features can better describe the quality of tandem mass spectra and hence improve the performance of tandem mass spectrum quality assessment.

Keywords: feature selection; SVM-RFE; tandem mass spectra; quality assessment; proteomics; recursive feature elimination; support vector machines; SVM.

DOI: 10.1504/IJDMB.2011.038578

International Journal of Data Mining and Bioinformatics, 2011 Vol.5 No.1, pp.73 - 88

Received: 14 Aug 2009
Accepted: 14 Aug 2009
Published online: 24 Jan 2015 *

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