Locality Kernels for Protein Classification

  • Evgeni Tsivtsivadze
  • Jorma Boberg
  • Tapio Salakoski
Conference paper

DOI: 10.1007/978-3-540-74126-8_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4645)
Cite this paper as:
Tsivtsivadze E., Boberg J., Salakoski T. (2007) Locality Kernels for Protein Classification. In: Giancarlo R., Hannenhalli S. (eds) Algorithms in Bioinformatics. WABI 2007. Lecture Notes in Computer Science, vol 4645. Springer, Berlin, Heidelberg

Abstract

We propose kernels that take advantage of local correlations in sequential data and present their application to the protein classification problem. Our locality kernels measure protein sequence similarities within a small window constructed around matching amino acids. The kernels incorporate positional information of the amino acids inside the window and allow a range of position dependent similarity evaluations. We use these kernels with regularized least-squares algorithm (RLS) for protein classification on the SCOP database. Our experiments demonstrate that the locality kernels perform significantly better than the spectrum and the mismatch kernels. When used together with RLS, performance of the locality kernels is comparable with some state-of-the-art methods of protein classification and remote homology detection.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Evgeni Tsivtsivadze
    • 1
  • Jorma Boberg
    • 1
  • Tapio Salakoski
    • 1
  1. 1.Turku Centre for Computer Science (TUCS), Department of Information Technology, University of Turku, Joukahaisenkatu 3-5 B, FIN-20520 TurkuFinland

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