Locality kernels for protein classification

Authors:
Evgeni Tsivtsivadze;Jorma Boberg;Tapio Salakoski
Affiliations:
Turku Centre for Computer Science, Department of Information Technology, University of Turku, Turku, Finland;Turku Centre for Computer Science, Department of Information Technology, University of Turku, Turku, Finland;Turku Centre for Computer Science, Department of Information Technology, University of Turku, Turku, Finland
Venue:
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
Year:
2007

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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.