Protein homology detection using string alignment kernels

Bioinformatics

Volumn 20, Issue 11, 2004, Pages 1682-1689

  Saigo, Hiroto ^a   Vert, Jean Philippe ^b   Ueda, Nobuhisa ^a   Akutsu, Tatsuya ^a  

^a KYOTO UNIVERSITY   (Japan)

^b PSL RESEARCH UNIVERSITY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTIC METHOD; ARTICLE; CLASSIFICATION; CONTROLLED STUDY; DATA ANALYSIS; EXPRESSION VECTOR; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; MEASUREMENT; PRIORITY JOURNAL; PROTEIN FAMILY; PROTEIN STRUCTURE; SCORING SYSTEM; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY;

EID: 4444273377     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/bth141     Document Type: Article

References (27)

1. Altschul,S., Gish,W., Miller,W., Myers,E. and Lipman,D. (1990) A basic local alignment search tool. J. Mol. Biol., 215, 403-410.
2. Altschul,S., Madden,T., Schaffer,A., Zhang,J., Zhang,Z., Miller,W. and Lipman,D. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389-3402.
3. Baldi,P., Chauvin,Y., Hunkapiller,T. and McClure,M. (1994) Hidden Markov models of biological primary sequence information. Proc. Natl Acad. Sci., USA, 91, 1053-1063.
4. Berg,C., Christensen,J. and Ressel,P. (1984) Harmonic Analysis on Semigroups. Springer-Verlag, New York.
5. Boser,B.E., Guyon,I.M. and Vapnik,V.N. (1992) A training algorithm for optimal margin classifiers. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. ACM Press, pp. 144-152.
6. Cristianini,N. and Shawe-Taylor,J. (2000) An introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge.
7. Durbin,R., Eddy,S., Krogh,A. and Mitchison,G. (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge.
8. Golub,G.H. and Loan,C.F.V. (1996) Matrix Computations. John Hopkins University Press.
9. Gribskov,M., Lüthy,R. and Eisenberg,D. (1990) Profile analysis. Meth. Enzymol., 183, 146-159.
10. Gribskov,M. and Robinson,N. (1996) Use of receiver operating characteristic (ROC) analysis to evaluate sequence matching. Comput Chem., 20, 25-33.
11. Haussler,D. (1999) Convolution kernels on discrete structures. Technical Report. University of California, Santa Cruz.
12. Jaakkola,T., Diekhans,M. and Haussler,D. (2000) A discriminative framework for detecting remote protein homologies. J. Comput. Bio., 7, 95-114.
13. Kanehisa,M., Goto,S., Kawashima,S. and Nakaya,A. (2002) The KEGG databases at GenomeNet. Nucleic Acids Res., 30, 42-46.
14. Karplus,K., Barrett,C. and Hughey,R. (1998) Hidden markov models for detecting remote protein homologies. Bioinformatics, 14, 846-856.
15. Krogh,A., Brown,M., Mian,I., Sjolander,K. and Haussler,D. (1994) Hidden Markov models in computational biology: applications to protein modeling. J. Mol. Biol., 235, 1501-1531.
16. Leslie,C., Eskin,E. and Noble,W.S. (2002) The spectrum kernel: a string kernel for SVM protein classification. In Altman,R.B., Dunker,A.K., Hunter,L., Lauerdale,K. and Klein,T.E. (eds), Proceedings of the Pacific Symposium on Biocomputing 2002. World Scientific, pp. 564-575.
17. Leslie,C., Eskin,E., Weston,J. and Noble,W.S. (2003) Mismatch string kernels for SVM protein classification. In Becker,S., Thrun,S. and Obermayer,K. (eds), Advances in Neural Information Processing Systems 15. MIT Press, Cambridge.
18. Liao,L. and Noble,W.S. (2002) Combining pairwise sequence similarity and support vector machines for remote protein homology detection. Proceedings of the Sixth International Conference on Computational Molecular Biology. ACM Press, pp. 225-232.
19. Murzin,A., Brenner,S., Hubbard,T. and Chothia,C. (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247, 536-540.
20. Pearson,W. (1990) Rapid and sensitive sequence comparisons with FASTP and FASTA. Meth. Enzymol., 183, 63-98.
21. Schölkopf,B. and Smola,A.J. (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA.
22. Schölkopf,B., Tsuda,K. and Vert,J.-P. (2004) Kernel Methods in Computational Biology.MIT Press, Cambridge.
23. Schölkopf,B., Weston,J., Eskin,E., Leslie,C. and Noble,W.S. (2002) A kernel approach for learning from almost orthogonal patterns. In Elomaa,T., Mannila,H. and Toivonen,H. (eds), Proceedings of ECML 2002, 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, Lecture Notes in Computer Science, 2430. Springer, pp. 511-528.
24. Smith,T. and Waterman,M. (1981) Identification of common molecular subsequences. J. Mol. Bio., 147, 195-197.
25. Vapnik,V.N. (1998) Statistical Learning Theory. Wiley, New York.
26. Vert,J.-P., Saigo,H. and Akutsu,T. (2004) Convolution and local alignment kernels. In Schölkopf,B., Tsuda,K. and Vert,J.-P. (eds), Kernel Methods in Computational Biology. MIT Press, pp. 131-154.
27. Watkins,C. (2000) Dynamic alignment kernels. In Smola,A., Bartlett,P., Schölkopf,B. and Schuurmans,D. (eds), Advances in Large Margin Classifiers. Cambridge, MA, MIT Press, pp. 39-50.