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Feature Extraction from Mass Spectra for Classification of Pathological States

  • Alexandros Kalousis
  • Julien Prados
  • Elton Rexhepaj
  • Melanie Hilario
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3721)

Abstract

Mass spectrometry is becoming an important tool in proteomics. The representation of mass spectra is characterized by very high dimensionality and a high level of redundancy. Here we present a feature extraction method for mass spectra that directly models for domain knowledge, reduces the dimensionality and redundancy of the initial representation and controls for the level of granularity of feature extraction by seeking to optimize classification accuracy. A number of experiments are performed which show that the feature extraction preserves the initial discriminatory content of the learning examples.

Keywords

Feature Extraction Peak Detection Discriminatory Information Initial Representation Spatial Redundancy 
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 2005

Authors and Affiliations

  • Alexandros Kalousis
    • 1
  • Julien Prados
    • 1
  • Elton Rexhepaj
    • 1
  • Melanie Hilario
    • 1
  1. 1.Computer Science DepartmentUniversity of GenevaGeneveSwitzerland

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