Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts

  • Britta Mersch
  • Tobias Glasmachers
  • Peter Meinicke
  • Christian Igel
Conference paper

DOI: 10.1007/11840930_86

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)
Cite this paper as:
Mersch B., Glasmachers T., Meinicke P., Igel C. (2006) Evolutionary Optimization of Sequence Kernels for Detection of Bacterial Gene Starts. In: Kollias S., Stafylopatis A., Duch W., Oja E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg

Abstract

Oligo kernels for biological sequence classification have a high discriminative power. A new parameterization for the K-mer oligo kernel is presented, where all oligomers of length K are weighted individually. The task specific choice of these parameters increases the classification performance and reveals information about discriminative features. For adapting the multiple kernel parameters based on cross-validation the covariance matrix adaptation evolution strategy is proposed. It is applied to optimize the trimer oligo kernel for the detection of prokaryotic translation initiation sites. The resulting kernel leads to higher classification rates, and the adapted parameters reveal the importance for classification of particular triplets, for example of those occurring in the Shine-Dalgarno sequence.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Britta Mersch
    • 1
  • Tobias Glasmachers
    • 2
  • Peter Meinicke
    • 3
  • Christian Igel
    • 2
  1. 1.German Cancer Research CenterHeidelbergGermany
  2. 2.Institut für NeuroinformatikRuhr-Universität BochumBochumGermany
  3. 3.Institut für Mikrobiologie und Genetik, Abteilung für BioinformatikGeorg-August-Universität GöttingenGöttingenGermany

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