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Bioinformatics Advance Access originally published online on February 20, 2009
Bioinformatics 2009 25(8):1076-1077; doi:10.1093/bioinformatics/btp094
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting

Jan Gertheiss * and Gerhard Tutz

Department of Statistics, Ludwig-Maximilians-Universität, Munich D-80799, Germany

*To whom correspondence should be addressed.


  Abstract

Summary: When feature selection in mass spectrometry is based on single m/z values, problems arise from the fact that variability is not only in vertical but also in horizontal direction, i.e. also slightly differing m/z values may correspond to the same feature. Hence, we propose to use the full spectra as input to a classifier, but to select small groups – or blocks – of adjacent m/z values, instead of single m/z values only. For that purpose we modify the LogitBoost to obtain a version of the so-called blockwise boosting procedure for classification. It is shown that blockwise boosting has high potential in predictive proteomics.

Availability: R-code is freely available at http://www.statistik.lmu.de/~gertheiss/research.html.

Contact: jan.gertheiss{at}stat.uni-muenchen.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on October 15, 2008; revised on January 30, 2009; accepted on February 16, 2009

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