Improved protein fold assignment using support vector machines

International Journal of Bioinformatics Research and Applications

Volumn 1, Issue 3, 2005, Pages 319-334

  Langlois, Robert E ^a   Diec, Alice ^a   Perisic, Ognjen ^a   Dai, Yang ^a   Lu, Hui ^a  

^a University of Illinois at Chicago   (United States)

Author keywords

fold recognition; machine learning; proteomics; structure prediction; support vector machines

Indexed keywords

PROTEIN;

AMINO ACID SEQUENCE; CHEMISTRY; COMPUTER PROGRAM; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE;

AMINO ACID SEQUENCE; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; SUPPORT VECTOR MACHINE;

EID: 29144504495     PISSN: 17445485     EISSN: 17445493     Source Type: Journal    
DOI: 10.1504/IJBRA.2005.007909     Document Type: Article

References (30)

1. Baldi, P., Brunak, S., Chauvin, Y., Andersen, C.A.F. and Nielsen, H. (2000) ‘Assessing the accuracy of prediction algorithms for classification: an overview’, Bioinformatics, Vol. 16, No. 5, pp.412–424.
2. Bonneau, R., Tsai, J., Ruczinski, I., Chivian, D., Strauss, C.E.M. and Baker, D. (2001) ‘Rosetta in CASP4: progress in abinitio protein structure prediction’, Proteins: Structure, Function, and Genetics, Vol. 45. No. S5, pp.119–126.
3. Brenner, S.E., Koehl, P. and Levitt, M. (2000) ‘The ASTRAL compendium for protein structure and sequence analysis’, Nucl. Acids. Res., Vol. 28, No. 1, pp.254–256.
4. Brown, M.P.S., Grundy, W.N., Lin, D., Cristianini, N., Sugnet, C.W., Furey, T. S., Ares, M. Jr. and Haussler, D. (2000) ‘Knowledge-based analysis of microarray gene expression data by using support vector machines’, PNAS, Vol. 97, No. 1, pp.262–267.
5. Chang, C-C. and Lin, C-J. (2001) LIBSVM: A Library for Support Vector Machines, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
6. Cortes, C. and Vapnik, V. (1995) ‘Support-vector networks’, Machine Learning, Vol. 20, No. 3, pp.273–297.
7. Cristianini, N. and Shawe-Taylor, J. (1999) An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, UK.
8. Ding, C.H.Q. and Dubchak, I. (2001) ‘Multi-class protein fold recognition using support vector machines and neural networks’, Bioinformatics, Vol. 17, No. 4, pp.349–358.
9. Dubchak, I., Muchnik, I., Mayor, C., Dralyuk, I. and Kim, S-H. (1999) ‘Recognition of a protein fold in the context of the structural classification of proteins (SCOP) classification’, Proteins: Structure, Function, and Genetics, Vol. 4, No. 35, pp.401–407.
10. Hou, Y.N., Hsu, W., Lee, M.L. and Bystroff, C. (2004) ‘Remote homolog detection using local sequence-structure correlations’, Proteins-Structure Function and Bioinformatics, Vol. 57, No. 3, pp.518–530.
11. Jaakkola, T., Diekhans, M. and Haussler, D. (1999) ‘Using the Fisher Kernel method to detect remote protein homologies’, Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology (ISMB-99) held in 6–10 August, Heidelberg, Germany.
12. Jaakkola, T., Diekhans, M. and Haussler, D. (2000) ‘A discriminative framework for detecting remote protein homologies’, Journal of Computational Biology, Vol. 7, No. 1, pp.95–114.
13. Joachims, T. (1998) ‘Text categorization with support vector machines: learning with many relevant features’, European Conference on Machine Learning (ECML-98), 21–24 April, Dorint-Parkhotel, Chemnitz, Germany.
14. Jones, D.T. (1999) ‘Protein secondary structure prediction based on position-specific scoring matrices’, Journal of Molecular Biology, Vol. 292, pp.195–202.
15. Kabsch, W. and Sander, C. (1983) ‘Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features’, Biopolymers, Vol. 22, No. 12, pp.2577–2637.
16. Karplus, K., Barrett, C. and Hughey, R. (1998) ‘Hidden Markov models for detecting remote protein homologies’, Bioinformatics, Vol. 14, No. 10, pp.846–856.
17. Kihara, D., Lu, H., Kolinski, A. and Skolnick, J. (2001) ‘TOUCHSTONE: an ab initio protein structure prediction method that uses threading-based tertiary restraints’, Proceedings of National Academy ofSciences (USA), Vol. 98, No. 18, pp.10125–10130.
18. Leslie, C., Eskin, E., Cohen, A., Weston, J. and Noble, W.S. (2002) Mismatch String Kernels for Discriminative Protein Classification, Neural Information Processing Systems.
19. Liao, L. and Noble, W.S. (2003) ‘Combining pairwise sequence similarity and support vector machines for detecting remote protein evolutionary and structural relationships’, Journal of Computational Biology, Vol. 10, Nol. 6, pp.857–868.
20. Liu, Y. (2004) ‘A comparative study on feature selection methods for drug discovery’, Journal of Chemical Information and Computer Sciences, Vol. 44, No. 5, pp.1823–1828.
21. Mcguffin, L.J., Bryson, K. and Jones, D.T. (2000) ‘The PSIPRED protein structure prediction server’, Bioinformatics, Vol. 16, No. 4, pp.404–405.
22. McGuffin, L.J. and Jones, D.T. (2003) ‘Improvement of the GenTHREADER method for genomic fold recognition’, Bioinformatics, Vol. 19, No. 7, pp.874–881.
23. Murzin, A.G., Brenner, S.E., Hubbard, T. and Chothia, C. (1995) ‘SCOP: a structural classification of proteins database for the investigation of sequences and structures’, Journal of Molecular Biology, Vol. 247, pp.536–540.
24. Platt, J.C., Cristianini, N. and Shawe-Taylor, J. (2000) Large Margin DAGs for Multiclass Classification, Advances in Neural Information Processing Systems.
25. Schapire, R.E. and Singer, Y. (1999) ‘Improved boosting algorithms using confidence-rated predictions’, Machine Learning, Vol. 37, No. 3, pp.297–336.
26. Skolnick, J. and Kihara, D. (2001) ‘Defrosting the frozen approximation: PROSPECTOR - a new approach to threading’, Proteins: Structure, Function, and Genetics, Vol. 42, No. 3, pp.319–331.
27. Sonchez, R. and Sali, A. (1997) ‘Evaluation of comparative protein structure modeling by MODELLER-3’, Proteins: Structure, Function and Genetics, Vol. 29, No. S1, pp.50–58.
28. Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T. and Vapnik, V. (2000) ‘Feature Selection for SVMs’, in Leen, T.K., Dietterich, T.G. and Tresp, V. (Eds.): Proceedings of Advances in Neural Information Processing Systems, The MIT Press, Cambridge, USA, Vol. 13, pp.668–674.
29. Xu, Y. and Xu, D. (2000) ‘Protein threading using PROSPECT: design and evaluation’, Proteins: Structure, Function, and Genetics, Vol. 40, No. 3, pp.343–354.
30. Yu, C-S., Wang, J-Y., Yang, J-M., Lyu, P-C., Lin, C-J. and Hwang, J-K. (2003) ‘Fine-grained protein fold assignment by support vector machines using generalized npeptide coding schemes and jury voting from multiple-parameters sets’, Proteins: Structure, Function and Genetics, Vol. 4, No. 50, pp.531–536.