Improved mass spectrometry peak intensity prediction by adaptive feature weighting

Authors:
Alexandra Scherbart;Wiebke Timm;Sebastian Böcker;Tim W. Nattkemper
Affiliations:
Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University;Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University and Intl. NRW Grad. School of Bioinformatics & Genome Research, Bielefeld University;Bioinformatics Group, Jena University;Biodata Mining & Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

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.