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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Spalding, J.D., Hoyle, D.C. (2005). Accuracy of String Kernels for Protein Sequence Classification. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_49
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DOI: https://doi.org/10.1007/11551188_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
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