Journal Article

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

Jan Gertheiss and Gerhard Tutz

in Bioinformatics

Volume 25, issue 8, pages 1076-1077
Published in print April 2009 | ISSN: 1367-4803
Published online February 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp094
Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting

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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@stat.uni-muenchen.de

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  1448 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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