Evolutionary optimization of sequence kernels for detection of bacterial gene starts

Authors:
Britta Mersch;Tobias Glasmachers;Peter Meinicke;Christian Igel
Affiliations:
German Cancer Research Center, Heidelberg, Germany;Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany;Institut für Mikrobiologie und Genetik, Abteilung für Bioinformatik, Georg-August-Universität Göttingen, Göttingen, Germany;Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Year:
2006

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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.