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.