Advertisement

A Hierarchical n-Grams Extraction Approach for Classification Problem

  • Faouzi Mhamdi
  • Ricco Rakotomalala
  • Mourad Elloumi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4879)

Abstract

We are interested in protein classification based on their primary structures. The goal is to automatically classify proteins sequences according to their families. This task goes through the extraction of a set of descriptors that we present to the supervised learning algorithms. There are many types of descriptors used in the literature. The most popular one is the n-gram. It corresponds to a series of characters of n-length. The standard approach of the n-grams consists in setting first the parameter n, extracting the corresponding ngrams descriptors, and in working with this value during the whole data mining process. In this paper, we propose an hierarchical approach to the n-grams construction. The goal is to obtain descriptors of varying length for a better characterization of the protein families. This approach tries to answer to the domain knowledge of the biologists. The patterns, which characterize the proteins’ family, have most of the time a various length. Our idea is to transpose the frequent itemsets extraction principle, mainly used for the association rule mining, in the n-grams extraction for protein classification context. The experimentation shows that the new approach is consistent with the biological reality and has the same accuracy of the standard approach.

Keywords

Data mining Protein Classification SVM Association Rules Frequent itemsets n-grams 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Fayyad, U., Shapiro, G., Smyth, P.: From data mining to knowledge discovery: A overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. MIT Press, Cambridge (1996)Google Scholar
  2. 2.
    Gibas, C., Jambeck, P.: Introduction à la bioinformatique, Oreilly (2002)Google Scholar
  3. 3.
    Karplus, K., Barrett, C., Hughey, R.: Hidden Markov models for detecting remote protein homologies. Bioinformatics 14, 846–856 (1998)CrossRefGoogle Scholar
  4. 4.
    Falquet, L., Pagni, M., Bucher, P., Hulo, N., Sigrist, C.J.A., Hofmann, K., Bairoch, A.: The PROSITE database, its status in 2002. Nucleic Acids Res. 30, 235–238 (2002)CrossRefGoogle Scholar
  5. 5.
    Sebastiani, F.: Machine learning in automated text categorisation. ACM Survey 34(1), 1–47 (2002)CrossRefGoogle Scholar
  6. 6.
    Mhamdi, F., Elloumi, M., Rakotomalala, R.: Textmining, features selection and datamining for proteins classification. In: IEEE/ICTTA 2004 (2004)Google Scholar
  7. 7.
    Mhamdi, F., Elloumi, M., Rakotomalala, R.: Desciptors Extraction for Proteins Classification. In: Proceeding of NCEI 2004, New Zealand (2004)Google Scholar
  8. 8.
    Lallich, S., Teytaud, O.: Évaluation et validation de l’intérêt des règles d’association, n°spécial Mesures de qualité pour la fouille des données, Revue des Nouvelles Technologies de l’Information, RNTI-E-1, 193–218 (2004)Google Scholar
  9. 9.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, Santiago, Chile (1994)Google Scholar
  10. 10.
    Murzin, G.A., Brenner, E.S., Hubbard, T., Chothia, C.: SCOP, a structural classification of proteins database for the investigation of sequences and structures. J. Mol. Bio. 247, 536–540 (1995)Google Scholar
  11. 11.
    Dietterich, T.: Approximate statistical tests for comparing supervised classification learning. Neural Computation journal 10(7), 1895–1924 (1999)CrossRefGoogle Scholar
  12. 12.
    Rakotomalala, R., Mhamdi, F.: Évaluation des méthodes supervisées pour la discrimination de protéines. In: Dans le proceeding de la conférence SFC 2006, Metz (2006)Google Scholar
  13. 13.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-base learning methods. Cambridge University Press, Cambridge (2000)CrossRefzbMATHGoogle Scholar
  14. 14.
    Eddy, S., Mitchison, G., Durbin, R.: Maximum discrimination hidden Markov models of sequences consensus. Journal of Computational Biology 2, 9–23 (1995)CrossRefGoogle Scholar
  15. 15.
    Krogh, A., Brown, M., Mian, I.S., Sjolander, K., Haussler, D.: Hidden Markov models in computational biology: Applications to protein modeling. Journal of Molecular Biology 235(5), 1501–1531 (1994)CrossRefGoogle Scholar
  16. 16.
    Vapnik, V.: The nature of statistical learning theory. Springer, HeidelbergGoogle Scholar
  17. 17.
    Guyon, I., Gupta, H.: An introduction to variable and feature selection. Journal of Machine Learning Reasearch, 157–1182 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Faouzi Mhamdi
    • 1
  • Ricco Rakotomalala
    • 2
  • Mourad Elloumi
    • 1
  1. 1.UTIC, Unité de recherche en Technologies de l’Information et de la CommunicationÉcole  Supérieure des Sciences et Techniques de TunisTunisie
  2. 2.Laboratoire ERICUniversité Lyon 2France

Personalised recommendations