Optimal Clustering-Based ART1 Classification in Bioinformatics: G-Protein Coupled Receptors Classification

  • Kyu Cheol Cho
  • Da Hye Park
  • Yong Beom Ma
  • Jong Sik Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

Protein sequence data have been revealed in current genome research and have been noticed in demand of classifier for new protein classification. This paper proposes the optimal clustering-based ART1 classifier for the GPCR data classification and processes the GPCR data classification. We focuses on a demand of optimal classifier system for protein sequence data classification. The optimal clustering-based ART1 classifier reduces processing cost for classification effectively. We compare classification success rate to those of Backpropagation Neural Network and SVM. In experimental result of the optimal clustering-based ART1 classifier, classification success rate of ClassA group is 99.7% and that of the others group is 96.6%. This result demonstrates that the optimal clustering-based ART1 classifier is useful to the GPCR data classification. The classification processing time of the optimal clustering-based ART1 classifier is the 27% less than that of the Backpropagation Neural Network and is the 39% less than that of the SVM in an optimal clustering rate which is 15%. And the classification processing time of the optimal clustering-based ART1 classifier is the 39% less than that of the optimal clustering-based ART1 classifier in a prediction success rate which is 96%. This result demonstrates that the optimal clustering-based ART1 classifier provides the high performance classification and the low processing cost in the GPCR data classification.

Keywords

Optimal Cluster Classification Success Cluster Rate ART1 Classifier ART1 Classification 
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. 1.
    Watson, S., Arkinstall, S.: The G-protein Linked Receptor Facts Book. Academic Press, Burlington (1994)Google Scholar
  2. 2.
    Georgiopulos, M., Heileman, G.L., Huang, J.: Properties of Learning Related to Pattern Diversity in ART1. Neural Networks 4, 751–757 (1991)CrossRefGoogle Scholar
  3. 3.
    Baxt, W.G.: Application of neural networks to clinical medicine. Lancet 346, 1135–1138 (1995)CrossRefGoogle Scholar
  4. 4.
    Finne, P., Finne, R., Stenman, U.H.: Neural network analysis of clinicopathological factorss in urological disease: a critical evaluation of available techniques. BJU Int. 88, 825–831 (2001)CrossRefGoogle Scholar
  5. 5.
    Lin, J.S., Ligomenides, P.A., Freedman, M.T., et al.: Application of artificial neural networks for reduction of false-positive detections in digital chest radiographs. In: Proc. Annu. Symp. Comput. Appl. Med. Care, pp. 434–438 (1993)Google Scholar
  6. 6.
    Wu, Y.C., Doi, K., Giger, M.L., et al.: Reduction of false positives in computerized detection of lung ndodules in chest radiographs using artificial neural networks, discriminant analysis, and a rule-based scheme. J. Digit Imaging 7, 196–207 (1994)CrossRefGoogle Scholar
  7. 7.
    Biganzoli, E., Boracchi, P., Mariani, L., et al.: Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat. Med. 17, 1169–1186 (1998)CrossRefGoogle Scholar
  8. 8.
    Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Welsey Publishing Co., Reading (1989)MATHGoogle Scholar
  9. 9.
    Jefferson, M.F., Narayanan, M.N., Lucas, S.B.: A neural network computer method to model the INR response of individual patients anticoagulated with warfarin. Br. J. Haematol. 89(1), 29 (1995)Google Scholar
  10. 10.
    Noguch, H., Hanai, T., Honda, H., Harrison, L.C., Kobayashi, T.: Fuzzy neural network-based prediction of the motif for MHC class II binding peptides. J. Biosci. Bioeng. 92, 227–231 (2001)CrossRefGoogle Scholar
  11. 11.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, NewYork (1995)MATHGoogle Scholar
  12. 12.
    Jaakkola, Haussler, D.: Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems, vol. 11, Morgan Kauffmann, San mateo (1998)Google Scholar
  13. 13.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, New York (2000)Google Scholar
  14. 14.
    Zeigler, B.P., et al.: The DEVS Environment for High-Performance Modeling and Simulation. IEEE C S & E 4(3), 61–71 (1997)CrossRefGoogle Scholar
  15. 15.
    Zeigler, B.P., et al.: DEVS Framework for Modeling, Simulation, Analysis and Design of Hybrid Systems in Hybrid II. LNCS, pp. 529–551. Springer, Berlin (1996)Google Scholar
  16. 16.
    Horn, F., Weare, J., Beukers, M.W., Horsch, S., Bairoch, A., Chen, W., Edvardsen, O., Campagne, F., Vriend, G., Gpcrdb, G.: An information system for g protein-coupled receptors. Nucleic Acids Res. 26, 277–281 (1998)CrossRefGoogle Scholar
  17. 17.
    Thompson, D.J., Higgins, G.D., Gibson, T.J.: CLUSTAL W:improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994)CrossRefGoogle Scholar
  18. 18.
    Jaakkola, T., Diekhans, M., Haussler, D.: A discriminative framework for detecting remote protein homologies. J. Comput. Biol. 7 (2000)Google Scholar
  19. 19.
    Poggio, T., Girosi, F.: Networks for Approximation and Learning. Proc. IEEE 78, 1481–1497 (1990)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyu Cheol Cho
    • 1
  • Da Hye Park
    • 1
  • Yong Beom Ma
    • 1
  • Jong Sik Lee
    • 1
  1. 1.School of Computer Science and EngineeringInha UniversityIncheonSouth Korea

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