Neural Network Approach for Mass Spectrometry Prediction by Peptide Prototyping

  • Alexandra Scherbart
  • Wiebke Timm
  • Sebastian Böcker
  • Tim W. Nattkemper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4669)


In todays bioinformatics, Mass spectrometry (MS) is the key technique for the identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to better understanding of spectrometry data and improved spectrum evaluation. We propose a neural network architecture of Local Linear Map (LLM)-type for peptide prototyping and learning locally tuned regression functions for peak intensity prediction in MALDI-TOF mass spectra. We obtain results comparable to those obtained by ν-Support Vector Regression and show how the LLM learning architecture provides a basis for peptide feature profiling and visualisation.


Root Mean Square Error Support Vector Regression Neural Network Approach Prototype Vector Learning Architecture 
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 2007

Authors and Affiliations

  • Alexandra Scherbart
    • 1
  • Wiebke Timm
    • 1
    • 2
  • Sebastian Böcker
    • 3
  • Tim W. Nattkemper
    • 1
  1. 1.Applied Neuroinformatics Group, Faculty of Technology, Bielefeld University, Postfach 10 01 31, 33501 Bielefeld 
  2. 2.Intl. NRW Graduate School of Bioinformatics and Genome Research, Bielefeld University 
  3. 3.Bioinformatics Group, Jena University 

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