PUB - Publikationen an der Universität Bielefeld

Mass spectrometry (MS) is a key technique for the analysis and identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to a better understanding of spectrometry data and improved spectrum evaluation. The goal is to model the relationship between peptides and peptide peak heights in MALDI-TOF mass spectra, only using the peptide’s sequence information and the chemical properties. To cope with this high dimensional data, we propose a regression based combination of feature weightings and a linear predictor to focus on relevant features. This offers simpler models, scalability, and better generalization. We show that the overall performance utilizing the estimation of feature relevance and re-training compared to using the entire feature space can be improved.