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Protein Fold Recognition with Combined SVM-RDA Classifier

  • Wiesław Chmielnicki
  • Katarzyna Sta̧por
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6076)

Abstract

Predicting the three-dimensional (3D) structure of a protein is a key problem in molecular biology. It is also an interesting issue for statistical methods recognition. There are many approaches to this problem considering discriminative and generative classifiers. In this paper a classifier combining the well-known Support Vector Machine (SVM) classifier with Regularized Discriminant Analysis (RDA) classifier is presented. It is used on a real world data set. The obtained results improve previously published methods.

Keywords

Support Vectore Machine Statistical classifiers RDA classifier protein fold recognition 

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References

  1. 1.
    Baldi, P., Brunak, S., Chauvin, Y., Andersen, C., Nielsen, H.: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412–424 (2000)CrossRefGoogle Scholar
  2. 2.
    Prevost, L., Qudot, L., Moises, A., Michel-Sendis, C., Milgram, M.: Hybrid generative/disciminative classifier for unconstrained character recognition. Pattern Recognition Letters 26, 1840–1848 (2005)CrossRefGoogle Scholar
  3. 3.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  4. 4.
    Chothia, C.: One thousand families for the molecular biologist. Nature 357, 543–544 (1992)CrossRefGoogle Scholar
  5. 5.
    Ding, C.H., Dubchak, I.: Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349–358 (2001)CrossRefGoogle Scholar
  6. 6.
    Dubchak, I., Muchnik, I., Holbrook, S.R., Kim, S.H.: Prediction of protein folding class using global description of amino acid sequence. Proc. Natl. Acad. Sci. USA 92, 8700–8704 (1995)CrossRefGoogle Scholar
  7. 7.
    Dubchak, I., Muchnik, I., Kim, S.H.: Protein folding class predictor for SCOP: approach based on global descriptors. In: Proceedings ISMB (1997)Google Scholar
  8. 8.
    Lo Conte, L., Ailey, B., Hubbard, T.J.P., Brenner, S.E., Murzin, A.G., Chotchia, C.: SCOP: a structural classification of protein database. Nucleic Acids Res. 28, 257–259 (2000)CrossRefGoogle Scholar
  9. 9.
    Shen, H.B., Chou, K.C.: Ensemble classifier for protein fold pattern recognition. Bioinformatics 22, 1717–1722 (2006)CrossRefGoogle Scholar
  10. 10.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefzbMATHGoogle Scholar
  11. 11.
    Vural, V., Dy, J.G.: A hierarchical method for multi-class support vector machines. In: Proceedings of the twenty-first ICML, Banff, Alberta, Canada, July 4-8, p. 105 (2004)Google Scholar
  12. 12.
    Wang, L., Shen, X.: Multi-category support vector machines, feature selection and solution path. Statistica Sinica 16, 617–633 (2006)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Nanni, L.: A novel ensemble of classifiers for protein fold recognition. Neurocomputing 69, 2434–2437 (2006)CrossRefGoogle Scholar
  14. 14.
    Okun, O.: Protein fold recognition with k-local hyperplane distance nearest neighbor algorithm. In: Proceedings of the Second European Workshop on Data Mining and Text Mining in Bioinformatics, Pisa, Italy, pp. 51–57, September 24 (2004)Google Scholar
  15. 15.
    Bologna, G., Appel, R.D.: A comparison study on protein fold recognition. In: Proceedings of the ninth ICONIP, Singapore, November 18-22, vol. 5, pp. 2492–2496 (2002)Google Scholar
  16. 16.
    Pal, N.R., Chakraborty, D.: Some new features for protein fold recognition. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 1176–1183. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  17. 17.
    Chung, I.F., Huang, C.D., Shen, Y.H., Lin, C.T.: Recognition of structure classification of protein folding by NN and SVM hierarchical learning architecture. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, pp. 1159–1167. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Hobohm, U., Sander, C.: Enlarged representative set of Proteins. Protein Sci. 3, 522–524 (1994)CrossRefGoogle Scholar
  19. 19.
    Hobohm, U., Scharf, M., Schneider, R., Sander, C.: Selection of a representative set of structures from the Brookhaven. Protein Bank Protein Sci. 1, 409–417 (1992)CrossRefGoogle Scholar
  20. 20.
    Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAGs for Multiclass Classification. In: Proceedings of Neural Information Processing Systems, NIPS 1999, pp. 547–553 (2000)Google Scholar
  21. 21.
    Fei, B., Liu, J.: Binary Tree of SVM: A New Fast Multiclass Training and Classification Algorithm. IEEE Transaction on Neural Networks 17(3) (2006)Google Scholar
  22. 22.
    Kijsirikul, B., Ussivakul, N.: Multiclass support vector machines using adaptive directed acyclic graph. In: Proceedings of IJCNN, pp. 980–985 (2002)Google Scholar
  23. 23.
    Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)zbMATHGoogle Scholar
  24. 24.
    Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Annals of Statistics 26(2), 451–471 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Dietterich, T.G., Bakiri, G.: Solving multiclass problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)zbMATHGoogle Scholar
  26. 26.
    Friedman, J.H.: Regularized Discriminant Analysis. Journal of the American Statistical Association 84(405), 165–175 (1989)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Liu, C.L., Fujisawa, H.: Classification and Learning for Character Recognition: Comparison of Methods and Remaining Problems. In: Proc. Int. Workshop on Neural 21 Networks and Learning in Document Analysis and Recognition, Seoul, Korea (August 2005)Google Scholar
  28. 28.
    Gori, M., Scarselli, F.: Are multilayer perceptrons adequate for pattern recognition and verification? IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1121–1132 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wiesław Chmielnicki
    • 1
  • Katarzyna Sta̧por
    • 2
  1. 1.Faculty of Physics, Astronomy and Applied Computer ScienceJagiellonian UniversityPoland
  2. 2.Institute of Computer ScienceSilesian University of TechnologyPoland

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