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Identification of Peptides with Deviating Regulation Factors Using a Robust Clustering Scheme

  • Natalia Novoselova
  • Frank Klawonn
  • Thorsten Johl
  • Tobias Reinl
  • Lothar Jänsch
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)

Abstract

A new approach to clustering likelihood curves is introduced which is based on the maximal density estimator algorithm. The clustered objects are the results of the analysis of mass spectrometry data and represent regulatory information of peptides, which belong to the same protein. The aim of the research is to reveal peptides within a protein sequence that show deviating regulation factors, caused either by the presence of noise in the measurements, the assignment of a peptide to a wrong protein or a modification of a peptide. The proposed approach allows arranging all the studied proteins into two groups: those, consisting of a single cluster of peptides and those with more than one cluster or with one or more outlier peptides with a regulation differing from the main cluster of peptides belonging to the protein.

Keywords

Phosphorylation Site Regulation Factor Single Cluster Curve Number Mass Spectrometry Data 
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 2011

Authors and Affiliations

  • Natalia Novoselova
    • 1
  • Frank Klawonn
    • 2
    • 3
  • Thorsten Johl
    • 3
  • Tobias Reinl
    • 3
  • Lothar Jänsch
    • 3
  1. 1.Laboratory of BioinformaticsUnited Institute of Informatics Problems, NAS BelarusMinskBelarus
  2. 2.Department of Computer ScienceOstfalia University of Applied SciencesWolfenbuettelGermany
  3. 3.Cellular Proteomics GroupHelmholtz Centre for Infection ResearchBraunschweigGermany

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