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SVM-Based Classification of Distant Proteins Using Hierarchical Motifs

  • Jérôme Mikolajczack
  • Gérard Ramstein
  • Yannick Jacques
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3177)

Abstract

This article presents a discriminative approach to the protein classification in the particular case of remote homology. The protein family is modelled by a set M of motifs related to the physicochemical properties of the residues. We propose an algorithm for discovering motifs based on the ascending hierarchical classification paradigm. The set M defines a feature space of the sequences: each sequence is transformed into a vector that indicates the possible presence of the motifs belonging to M. We then use the SVM learning method to discriminate the target family. Our hierarchical motif set specifically modelises interleukins among all the structural families of the SCOP database. Our method yields a significantly better remote protein classification compared to spectrum kernel techniques.

Keywords

Support Vector Machine Scop Database Support Vector Machine Algorithm String Kernel Linear Time Complexity 
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 2004

Authors and Affiliations

  • Jérôme Mikolajczack
    • 1
  • Gérard Ramstein
    • 2
  • Yannick Jacques
    • 1
  1. 1.Département de CancérologieInstitut de BiologieNantes Cedex
  2. 2.LINA Ecole polytechnique de l’Université de NantesNantes Cedex 3

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