Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Combining statistical models for protein secondary structure prediction

  • Poster Presentations 1
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN 96 (ICANN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

Included in the following conference series:

Abstract

We investigate the problem of combining experts to predict the secondary structure of globular proteins. We first present two different statistical models for this task. We then analyse an efficient linear combination technique, this sheds light on unexplained phenomena frequently encountered in practice for ensemble methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Qian, N. and Sejnowski, T.J. (1988). Predicting the Secondary Structure of Globular Proteins Using Neural Network Models. J. Mol. Biol., 202, 865–884.

    Google Scholar 

  2. Eisenberg, D., Wilcox, W. and Eshita, S. (1987). Hydrophobic moments as tools for analysis of protein sequences and structures. In Proteins: structure and function. Edited by James J. L'Italien, Plenum Press, 1987, 425–436.

    Google Scholar 

  3. Colloc'h, N., Etchebest, C., Thoreau, E., Henrissat, B. and Mornon, J.P. (1993). Comparison of three algorithms for the assignment of secondary structure in proteins: the advantages of a consensus assignment. Protein Engineering, vol. 6, 377–382.

    Google Scholar 

  4. Kabsch, W. and Sander, C. (1983). Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, vol. 22, N∘12, 2577–2637.

    Google Scholar 

  5. Matthews, B.W. (1975). Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta, 405, 442–451.

    Google Scholar 

  6. Bahadur, R.R. (1961). A Representation of the Joint Distribution of Responses to n Dichotomous Items. In Studies in Item Analysis and Prediction, chapt. 9, 158–169, Stanford University Press.

    Google Scholar 

  7. Zhang, X., Mesirov, J.P. and Waltz, D.L. (1992). Hybrid System for Protein Secondary Structure Prediction. J. Mol. Biol., 225, 1049–1063.

    Google Scholar 

  8. Breiman, L. (1992). Stacked Regressions. Technical Report N∘ 367, August 1992, Department of statistics, University of California, Berkeley.

    Google Scholar 

  9. Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, vol. 5, 241–259.

    Google Scholar 

  10. Rost, B. and Sander, C. (1994). Combining Evolutionary Information and Neural Networks to Predict Protein Secondary Structure. Proteins, 19, 55–72.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LAFORIA IBP-UA CNRS 1095, Université Pierre et Marie Curie Tour 46-00, Boîte 169, 4, Place Jussieu, 75252, Paris cedex 05, France

    Yann Guermeur & Patrick Gallinari

Authors
  1. Yann Guermeur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Gallinari
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Guermeur, Y., Gallinari, P. (1996). Combining statistical models for protein secondary structure prediction. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_102

Download citation

  • DOI: https://doi.org/10.1007/3-540-61510-5_102

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation