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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jaakkola, T., Diekhans, M., Haussler, D.: A discriminative framework for detecting remote protein homologies. Journal of Computational Biology 7, 95–114 (2000)
Liao, L., Noble, W.S.: Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships. Journal of Computational Biology 10, 857–868 (2003)
Leslie, C., Eskin, E., Noble, W.S.: The spectrum kernel: A string kernel for svm protein classification. In: Pacific Symposium on Biocomputing, pp. 566–575 (2002)
Tsivtsivadze, E., Pahikkala, T., Boberg, J., Salakoski, T.: Locality-convolution kernel and its application to dependency parse ranking. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 610–618. Springer, Heidelberg (2006)
Hubbard, T.J.P., Murzin, A.G., Brenner, S.E., Chothia, C.: Scop: a structural classification of proteins database. Nucleic Acids Research 25, 236–239 (1997)
Leslie, C.S., Eskin, E., Cohen, A., Weston, J., Noble, W.S.: Mismatch string kernels for discriminative protein classification. Bioinformatics 20, 467–476 (2004)
Leslie, C., Kuang, R.: Fast string kernels using inexact matching for protein sequences. J. Mach. Learn. Res. 5, 1435–1455 (2004)
Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Amer. Math. Soc. Notice 50, 537–544 (2003)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, New York, USA (2004)
Pahikkala, T., Pyysalo, S., Ginter, F., Boberg, J., Järvinen, J., Salakoski, T.: Kernels incorporating word positional information in natural language disambiguation tasks. In: Russell, I., Markov, Z. (eds.) Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, Menlo Park, Ca., pp. 442–447. AAAI Press, Stanford, California, USA (2005)
Zien, A., Ratsch, G., Mika, S., Scholkopf, B., Lengauer, T., Muller, K.-R.: Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics 16, 799–807 (2000)
Haussler, D.: Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, UC Santa Cruz (1999)
Gribskov, M., Robinson, N.L.: Use of receiver operating characteristic (roc) analysis to evaluate sequence matching. Computers & Chemistry 20, 25–33 (1996)
Fawcett, T.: Roc graphs: Notes and practical considerations for data mining researchers. Technical Report HPL-2003-4, HP Labs (2003)
Kuang, R., Ie, E., Wang, K., Wang, K., Siddiqi, M., Freund, Y., Leslie, C.: Profile-based string kernels for remote homology detection and motif extraction. J. Bioinform. Comput. Biol. 3, 527–550 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsivtsivadze, E., Boberg, J., Salakoski, T. (2007). Locality Kernels for Protein Classification. In: Giancarlo, R., Hannenhalli, S. (eds) Algorithms in Bioinformatics. WABI 2007. Lecture Notes in Computer Science(), vol 4645. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74126-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-74126-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74125-1
Online ISBN: 978-3-540-74126-8
eBook Packages: Computer ScienceComputer Science (R0)