Accuracy of string kernels for protein sequence classification

Authors:
J. Dylan Spalding;David C. Hoyle
Affiliations:
School of Engineering, Computer Science & Mathematics, University of Exeter, Exeter, UK;School of Engineering, Computer Science & Mathematics, University of Exeter, Exeter, UK
Venue:
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Year:
2005

Citing 3
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Mismatch string kernels for discriminative protein classification

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Abstract

Determining protein sequence similarity is an important task for protein classification and homology detection. Typically this may be done using sequence alignment algorithms, yet fast and accurate alignment-free kernel based classifiers exist. Viewing sequences as a “bag of words”, we test a simple weighted string kernel, investigating the effects of k-mer length, sequence length and choice of weighting. We also extend the kernel to operate on the k-mer frequency representation of a sequence rather than the “bag of words” representation.