An Efficient Multi-class Support Vector Machine Classifier for Protein Fold Recognition

  • Wiesław Chmielnicki
  • Katarzyna Sta̧por
  • Irena Roterman-Konieczna
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 74)


Predicting the three-dimensional (3D) structure of a protein is a key problem in molecular biology. It is also interesting issue for statistical methods recognition. In this paper a multi-class Support Vector Machine (SVM) classifier is used on a real world data set. The SVM is a binary classifier and how to effectively extend a binary to the multi-class classifier case is still an on-going research problem. The new efficient approach is proposed in this paper. The obtained results are promising and reveal areas for possible further work.


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  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.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at,
  3. 3.
    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)Google Scholar
  4. 4.
    Dietterich, T.G., Bakiri, G.: Solving multiclass problems via error-correcting output codes. Journal of Artificial Intelligence Research 2, 263–286 (1995)zbMATHGoogle 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., Mayor, C., Dralyuk, I., Kim, S.H.: Recognition of protein fold in the context of the Structural Classification of Proteins (SCOP) classification. Proteins 35, 401–407 (1999)CrossRefGoogle Scholar
  7. 7.
    Fei, B., Liu, J.: Binary Tree of SVM: A New Fast Multiclass Training and Classification Algorithm. IEEE Transaction on neural networks 17(3) (May 2006)Google Scholar
  8. 8.
    Hastie, T., Tibshirani, R.: Classification by pairwise coupling. Annals of Statistics 26(2), 451–471 (1998)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Hobohm, U., Sander, C.: Enlarged representative set of Proteins. Protein Sci. 3, 522–524 (1994)CrossRefGoogle Scholar
  10. 10.
    Jinbai, L., Ben, F., Lihong, X.: Binary tree of Support Vector Machine in multi-class classification problem 3rd ICECE (2004)Google Scholar
  11. 11.
    Kijsirikul, B., Ussivakul, N.: Multiclass support vector machines using adaptive directed acyclic graph. In: Proceedings of IJCNN, pp. 980–985 (2002)Google Scholar
  12. 12.
    Lee, Y., Lee, C.K.: Classification of Multiple Cancer Types by Multicategory Support Vector Machines Using Gene Expression Data. Bioinformatics 19, 1132–1139 (2003)CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    Madzarov, G., Gjorgjevskij, D., Chorbev, I.: A multi-class SVM classifier utilizing decision tree. Informatica 33, 233–241 (2009)Google Scholar
  15. 15.
    Nanni, L.: A novel ensemble of classifiers for protein fold recognition. Neurocomputing 69, 2434–2437 (2006)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Shen, H.B., Chou, K.C.: Ensemble classifier for protein fold pattern recognition. Bioinformatics 22, 1717–1722 (2006)CrossRefGoogle Scholar
  18. 18.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  19. 19.
    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 04–08, p. 105 (2004)Google Scholar
  20. 20.
    Wang, L., Shen, X.: Multi-category support vector machines, feature selection and solution path. Statistica Sinica 16, 617–633 (2006)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wiesław Chmielnicki
    • 1
  • Katarzyna Sta̧por
    • 2
  • Irena Roterman-Konieczna
    • 3
  1. 1.Faculty of Physics, Astronomy and Applied Computer Science 
  2. 2.Institute of Computer ScienceSilesian University 
  3. 3.Faculty of Medicine, Department of Bioinformatics and TelemedicineJagiellonian University 

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