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

Authors:
Jiarui Ding;Jinhong Shi;Fang-Xiang Wu
Affiliations:
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
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2011

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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.