title: Supervised feature selection in mass spectrometry-based proteomic profiling by blockwise boosting.
abstract: 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.
authors.affiliation: Department of Statistics, Ludwig-Maximilians-Universität, Munich, Germany. jan.gertheiss@stat.uni-muenchen.de
authors.forename: Jan
authors.initials: J
authors.lastname: Gertheiss
authors.forename: Gerhard
authors.initials: G
authors.lastname: Tutz
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D013058:Mass Spectrometry / Q000379:methods*; D020543:Proteome / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*
chemical_list: D020543:Proteome