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Knowledge and Information Systems

, Volume 25, Issue 2, pp 327–343 | Cite as

Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints

  • Stephan Simmuteit
  • Frank-Michael SchleifEmail author
  • Thomas Villmann
  • Barbara Hammer
Regular Paper

Abstract

In this paper, we investigate the application of Evolving Trees (ET) for the analysis of mass spectrometric data of bacteria. Evolving Trees are extensions of self-organizing maps (SOMs) developed for hierarchical classification systems. Therefore, they are well suited for taxonomic problems such as the identification of bacteria. Here, we focus on three topics, an appropriate pre-processing and encoding of the spectra, an adequate data model by means of a hierarchical Evolving Tree and an interpretable visualization. First, the high dimensionality of the data is reduced by a compact representation. Here, we employ sparse coding, specifically tailored for the processing of mass spectra. In the second step, the topographic information which is expected in the fingerprints is used for advanced tree evaluation and analysis. We adapted the original topographic product for SOMs for ET to achieve a judgment of topography. Additionally we transferred the concept of U-matrix for evaluation of the separability of SOMs to their analog in ET. We demonstrate these extensions for two mass spectrometric data sets of bacteria fingerprints and show their classification and evaluation capabilities in comparison to state of the art techniques.

Keywords

Evolving tree Sparse coding Mass spectrometry Bacteria identification Prototype learning 

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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Stephan Simmuteit
    • 1
  • Frank-Michael Schleif
    • 1
    Email author
  • Thomas Villmann
    • 2
  • Barbara Hammer
    • 3
  1. 1.Medical Department, Computational Intelligence GroupUniversity LeipzigLeipzigGermany
  2. 2.Department of MPIUniversity of Applied Science MittweidaMittweidaGermany
  3. 3.Department of Computer Science, Computational Intelligence GroupClausthal UniversityClausthal-ZellerfeldGermany

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