Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

IAPR International Conference on Pattern Recognition in Bioinformatics

PRIB 2012: Pattern Recognition in Bioinformatics pp 153–165Cite as

  1. Home
  2. Pattern Recognition in Bioinformatics
  3. Conference paper
Application of the Multi-modal Relevance Vector Machine to the Problem of Protein Secondary Structure Prediction

Application of the Multi-modal Relevance Vector Machine to the Problem of Protein Secondary Structure Prediction

  • Nikolay Razin23,
  • Dmitry Sungurov23,
  • Vadim Mottl24,
  • Ivan Torshin24,
  • Valentina Sulimova25,
  • Oleg Seredin25 &
  • …
  • David Windridge26 
  • Conference paper
  • 1585 Accesses

  • 1 Citations

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7632)

Abstract

The aim of the paper is to experimentally examine the plausibility of Relevance Vector Machines (RVM) for protein secondary structure prediction. We restrict our attention to detecting strands which represent an especially problematic element of the secondary structure. The commonly adopted local principle of secondary structure prediction is applied, which implies comparison of a sliding window in the given polypeptide chain with a number of reference amino-acid sequences cut out of the training proteins as benchmarks representing the classes of secondary structure. As distinct from the classical RVM, the novel version applied in this paper allows for selective combination of several tentative window comparison modalities. Experiments on the RS126 data set have shown its ability to essentially decrease the number of reference fragments in the resulting decision rule and to select a subset of the most appropriate comparison modalities within the given set of the tentative ones.

Keywords

  • Protein secondary structure prediction
  • machine learning
  • multimodal relational pattern recognition
  • Relevance Vector Machine
  • controlled selectivity of reference objects
  • object-comparison modalities

Download conference paper PDF

References

  1. Branden, C., Tooze, J.: Introduction to Protein Structure, 2nd edn., p. 410. Garland Publishing, Inc., New York (1999)

    Google Scholar 

  2. Rost, B.: Protein secondary structure prediction continues to rise. Journal of Structural Biology 134(2-3), 204–218 (2001)

    CrossRef  Google Scholar 

  3. Yoo, P., Zhou, B., Zomaya, A.: Machine learning techniques for protein secondary structure prediction: An overview and evaluation. Current Bioinformatics 3(2), 74–86 (2008)

    CrossRef  Google Scholar 

  4. Critical Assessment of the Protein Structure Prediction. Protein Structure Prediction Center. Sponsored by the US National Library of Medicine (NIH/NLM), http://predictioncenter.org/http://predictioncenter.org/index.cgi?page=proceedings

  5. Aloy, P., Stark, A., Hadley, C., Russell, R.: Predictions without templates: new folds, secon-dary structure, and contacts in CASP5. Proteins 53(suppl. 6), 436–456 (2003)

    CrossRef  Google Scholar 

  6. Torshin, I.Y.: Bioinformatics in the Post-Genomic Era: The Role of Biophysics. Nova Biomedical Books, NY (2007) ISBN: 1-60021-048

    Google Scholar 

  7. Vapnik, V.: Statistical Learning Theory, p. 736. John-Wiley & Sons, Inc. (1998)

    Google Scholar 

  8. Ward, J., McGuffin, L., Buxton, B., Jones, D.: Secondary structure prediction with support vector machines. Bioinformatics 19(13), 1650–1655 (2003)

    CrossRef  Google Scholar 

  9. Bishop, C., Tipping, M.: Variational Relevance Vector Machines. In: Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 46–53. Morgan Kaufmann (2000)

    Google Scholar 

  10. Seredin, O., Mottl, V., Tatarchuk, A., Razin, N., Windridge, D.: Convex Support and Relevance Vector Machines for selective multimodal pattern recognition. In: The 21th International Conference on Pattern Recognition, Tsukuba Science City, Japan, November 11-15 (2012)

    Google Scholar 

  11. Engel, D., DeGrado, W.: Amino acid propensities are position-dependent throughout the length of α-helices. J. Mol. Biol. 337, 1195–1205 (2004)

    CrossRef  Google Scholar 

  12. Ni, Y., Niranjan, M.: Exploiting long-range dependencies in protein β-sheet secondary structure prediction. In: Proceedings of the 5th IAPR International Conference on Pattern Recognition in Bioinformatics, Nijmegen, The Netherlands, September 22-24, pp. 349–357 (2010)

    Google Scholar 

  13. Cole, C., Barber, J., Barton, G.: The Jpred 3 secondary structure prediction server. Nucl. Acids Res. 36 (suppl. 2), W197–W201 (2008)

    Google Scholar 

  14. Duin, R., Pekalska, E., de Ridder, D.: Relational discriminant analysis. Pattern Recognition Letters 20, 1175–1181 (1999)

    CrossRef  Google Scholar 

  15. Wang, L., Zhu, J., Zou, H.: The doubly regularized support vector machine. Statistica Sinica 16, 589–615 (2006)

    MathSciNet  MATH  Google Scholar 

  16. Dayhoff, M., Schwarts, R., Orcutt, B.: A model of evolutionary change in proteins. Atlas of Protein Sequences and Structures 5(suppl. 3), 345–352 (1978)

    Google Scholar 

  17. Sulimova, V., Mottl, V., Kulikowski, C., Muchnik, I.: Probabilistic evolutionary model for substitution matrices of PAM and BLOSUM families. DIMACS Technical Report 2008-16, Rutgers University, p. 17 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Moscow, Russia

    Nikolay Razin & Dmitry Sungurov

  2. Computing Center of the Russian Academy of Sciences, Moscow, Russia

    Vadim Mottl & Ivan Torshin

  3. Tula State University, Tula, Russia

    Valentina Sulimova & Oleg Seredin

  4. University of Surrey, Guildford, UK

    David Windridge

Authors
  1. Nikolay Razin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dmitry Sungurov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vadim Mottl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ivan Torshin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Valentina Sulimova
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Oleg Seredin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. David Windridge
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Medical Science, University of Tokyo, 4-6-1, Shirokanedai, 108-8639, Minato-ku, Tokyo, Japan

    Tetsuo Shibuya

  2. Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, 113-8654, Bunkyo-ku, Tokyo, Japan

    Hisashi Kashima

  3. Department of Comouter Science, Tokyo Institute of Technology, 2-12-1 Ookayamama, 152-8550, Meguro-ku, Tokyo, Japan

    Jun Sese

  4. Bioinformatics Project, National Institute of Biomedical Innovation, 7-6-8 Saito-Asagi, 567-0085, Suita, Osaka, Japan

    Shandar Ahmad

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Razin, N. et al. (2012). Application of the Multi-modal Relevance Vector Machine to the Problem of Protein Secondary Structure Prediction. In: Shibuya, T., Kashima, H., Sese, J., Ahmad, S. (eds) Pattern Recognition in Bioinformatics. PRIB 2012. Lecture Notes in Computer Science(), vol 7632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34123-6_14

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-34123-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34122-9

  • Online ISBN: 978-3-642-34123-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • The International Association for Pattern Recognition

    Published in cooperation with

    http://www.iapr.org/

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature