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Automatic Classification of Protein Structures Based on Convex Hull Representation by Integrated Neural Network

  • Yong Wang
  • Ling-Yun Wu
  • Xiang-Sun Zhang
  • Luonan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3959)

Abstract

The large scale deposited data and existing manual classification scheme make it possible to study the automatic classification of protein structures in machine learning framework. In this paper the classification system is constructed by an integrated feedforward neural network through incorporating the expert judgements and existing classification schemes into the learning procedure. Since different aspects of a protein structure may be relevant to various biological problems, the protein structure is represented by the convex hull of its backbone and geometric features are extracted. The training and prediction tests for different training sets in the class level of CATH indicate that the new automatic classification scheme is effective and efficient. Also the neural network model outperforms hidden markov model and support vector machine in the comparison experiment.

Keywords

Support Vector Machine Convex Hull Hide Markov Model Training Accuracy Validation Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (1998)Google Scholar
  2. Hubbard, T.J., Ailey, B., Brenner, S.E., Murzin, A.G., Chothia, C.: SCOP: A Structural Classification of Proteins Database. Nucleic Acids Research 27, 254–256 (1999)CrossRefGoogle Scholar
  3. Orengo, C.A., Michie, A.D., Jones, S., Jones, D.T., Swindells, M.B., Thornton, J.M.: CATH- A Hierarchic Classification of Protein Domain Structures. Structure 5, 1093–1108 (1997)CrossRefGoogle Scholar
  4. Holm, L., Sander, C.: Protein Structure Comparison by Alignment of Distance Matrices. Journal of Molecular Biology 233, 123–138 (1993)CrossRefGoogle Scholar
  5. Rogen, P., Fain, B.: Automatic Classification of Protein Structures by Using Gauss Integrals. Proceedings of the National Academy of Sciences 100, 119–124 (2003)CrossRefGoogle Scholar
  6. Hadley, C., Jones, D.T.: A Systematic Comparison of Protein Structure Classifications: SCOP, CATH and FSSP. Structure with Folding & Design 7, 1099–1112 (1999)Google Scholar
  7. Sierk, M.L., Pearson, W.R.: Sensitivity and Selectivity in Protein Structure Comparison. Protein Science 13, 773–785 (2004)CrossRefGoogle Scholar
  8. Zhang, X.S., Zhan, Z.W., Wang, Y., Wu, L.Y.: An Attempt to Explore the Similarity of Two Proteins by Their Surface Shapes. In: Operations Research and Its Applications, Lecture Notes in Operations Research, vol. 5, pp. 276–284. World Publishing Corporation, Beijing (2005)Google Scholar
  9. Zhang, X.S.: Neural Networks in Optimization. Nonconvex Optimization and Its Applications, vol. 46. Kluwer Academic Publishers, Dordrecht (2000)MATHGoogle Scholar
  10. Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/cjlin/libsvm
  11. Young, S.: The HTK Book version 3, Microsoft Corporation (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yong Wang
    • 1
    • 2
  • Ling-Yun Wu
    • 2
  • Xiang-Sun Zhang
    • 2
  • Luonan Chen
    • 1
  1. 1.Osaka Sangyo UniversityDaito, OsakaJapan
  2. 2.Academy of Mathematics and Systems ScienceCAS, BeijingChina

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