Prediction of Protein Secondary Structures of All Types Using New Hypersphere Machine Learning Method

  • Markku Siermala
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)


In this paper, we present a new hypersphere machine learning method and use it to predict all protein secondary structures. It finds sequences with sufficiently high homology. Prediction accuracy of the new method with protein secondary structures was good (average 89.3%). However, the method could not classify all test cases.


Secondary Structure Prediction Accuracy Secondary Structure Prediction Protein Secondary Structure Misclassification Rate 
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  1. 1.
    Baldi P., Brunak S., Frasconi P., Soda G., Pollastri G.: Exploiting the Past and the Future in Protein Secondary Structure Prediction. Bioinformatics 15 (1999) 937–946CrossRefGoogle Scholar
  2. 2.
    Rost B.: A Neural Network for Prediction of Protein Secondary Structure. In Fiesler, E. and Beale, R. (eds.), Handbook of Neural Computation. IOP Publishing and Oxford University Press (1997), pp. G4.1:1–9.Google Scholar
  3. 3.
    Ruggiero C., Sacile R., Rauch G.: Peptides Secondary Structure Prediction with Neural Networks: A Criterion for Building Appropriate Learning Sets. Trans. Biomed. Eng. 40 (1993) 1114–1121.CrossRefGoogle Scholar
  4. 4.
    Guermeur Y., Geourjon C., Gallinari P., Deleage G.: Improved Performance in Protein Structure Prediction by Inhomogeneous Score Combination. Bioinformatics 15 (1999) 413–421CrossRefGoogle Scholar
  5. 5.
    Hayward S., Collins J.: Limits on α-Helix Prediction With Neural Network Models. Proteins 14 (1992) 372–381CrossRefGoogle Scholar
  6. 6.
    Baldi P., Brunak S. (eds.): Bioinformatics: The Machine Learning Approach. The MIT Press, London (2000)Google Scholar
  7. 7.
    Kabsch W., Sander C.: Dictionary of Protein Secondary Structure: Pattern Recognition of Hydrogen-Bonded and Geometrical Features. Biopolymers 22 (1983) 2577–2637CrossRefGoogle Scholar
  8. 8.
    Mitchell T.: Machine Learning. McGraw-Hill, Singapore (1997)zbMATHGoogle Scholar
  9. 9.
    Berman H, Westbrook J., Feng Z., Gilliland G., Bhat T., Weissig H., Shindyalov I., Bourne P.: The Protein Data Bank. Nucleic Acids Research 28 (2000) 235–242.CrossRefGoogle Scholar
  10. 10.
    Siermala M., Juhola M., Vihinen M.: Neural Network Prediction of Polyproline Type II Secondary Structure. In Hasman et al. eds. Medical Infobahn for Europe, Proceedings of MIE2000 and GMDS2000, IOS Press 77 (2000) 475–479Google Scholar
  11. 11.
    Adzhubei A., Sternberg M.: Left-handed Polyproline II Helices Commonly Occur in Globular Proteins, J. Mol. Biol. 229 (1993) 472–493CrossRefGoogle Scholar
  12. 12.
    Laurikkala, J., Juhola, M., Lammi, S., Penttinen, J., Aukee, P.: Analysis of the Imputed Female Urinary Incontinence D67ata for the Evaluation of Expert System Parameters. Comput. Biol. Med., (2001) 31(4).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Markku Siermala
    • 1
  1. 1.Department of Computer ScienceUniversity of TampereFinland

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