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Improved Mass Spectrometry Peak Intensity Prediction by Adaptive Feature Weighting

  • Alexandra Scherbart
  • Wiebke Timm
  • Sebastian Böcker
  • Tim W. Nattkemper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)

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.

Keywords

Partial Little Square Feature Space Feature Weighting Learning Architecture Local Linear Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandra Scherbart
    • 1
  • Wiebke Timm
    • 1
    • 2
  • Sebastian Böcker
    • 3
  • Tim W. Nattkemper
    • 1
  1. 1.Biodata Mining & Applied Neuroinformatics Group, Faculty of TechnologyBielefeld UniversityGermany
  2. 2.Intl. NRW Grad. School of Bioinformatics & Genome ResearchBielefeld UniversityGermany
  3. 3.Bioinformatics GroupJena UniversityGermany

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