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Evolutionary Neuro-Fuzzy System for Protein Secondary Structure Prediction

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)

Abstract

Protein secondary structure prediction is an essential step for the understanding of both the mechanisms of folding and the biological function of proteins. Experimental evidences show that the native conformation of a protein is coded within its primary structure. This work investigates the benefits of combining genetic algorithms, fuzzy logic, and neural networks into a hybrid Evolutionary Neuro-Fuzzy System, especially for predicting a protein’s secondary structure directly from its primary structure. The proposed system will include more biological information such as protein structural class, solvent accessibility, hydrophobicity and physicochemical properties of amino acid residues to improve accuracy of protein secondary structure prediction. The proposed system will experiment on three-class secondary structure prediction of proteins, that is, alpha helix, beta sheet or coil. The experimental results indicate that the proposed method has the advantages of high precision, good generalization, and comprehensibility. The method also exhibits the property of rapid convergence in fuzzy rule generation.

Keywords

Artificial Neural Networks Fuzzy Logic Genetic Algorithms Protein Secondary Structure 

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References

  1. 1.
    Jang, J.-S.R.: ANFIS: Adaptive-Network-Based Fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics 23(0018-9472), 665–685 (1993)CrossRefGoogle Scholar
  2. 2.
    Baker, D., Sali, A.: Protein Structure Prediction and Structural genomics. Science 294(5540), 93–96 (2001)CrossRefGoogle Scholar
  3. 3.
    Mount, D.W.: Bioinformatics: Sequence and Genome Analysis. Gold Spring Harbor Laboratory PressGoogle Scholar
  4. 4.
    Sugeno, T., Yasukawa, M.: A fuzzy logic based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems 1(1), 7–31 (1993)CrossRefGoogle Scholar
  5. 5.
    Kawashima, S., Kenehisa, M.: AAIndex: Amino acid index database. Nucleic Acids Research 28, 374 (2000)CrossRefGoogle Scholar
  6. 6.
    Kasabov, N.K.: Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. MIT Press (1998)Google Scholar
  7. 7.
    Baldi, P., Brunak, S.: Bioinformatics: The machine learning approach. The MIT Press (2001)Google Scholar
  8. 8.
    Eberhart, R.C., Shi, Y.: Computational Intelligence: Concepts & Implementation. Morgan Kalfman Publishers (2007)Google Scholar
  9. 9.
    Takagi, H.: Introduction to Fuzzy Systems. Neural Networks, and Genetic AlgorithmsGoogle Scholar
  10. 10.
    Fausette, L.: Fundamentals of Neural Networks: Architectures, Algorithms, and ApplicationsGoogle Scholar
  11. 11.
    Weise, T.: Global Optimization Algorithms: Theory and Applications, 2nd edn (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dept. of Computer ApplicationsSwarnandhra College of Engineering & TechnologyNarasapurIndia
  2. 2.JNTUK, CRRao Advanced Institute for Mathematics, Statistics & Computer ScienceUniversity of Hyderabad CampusHyderabadIndia

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