EGEA : A New Hybrid Approach Towards Extracting Reduced Generic Association Rule Set (Application to AML Blood Cancer Therapy)

  • M. A. Esseghir
  • G. Gasmi
  • S. Ben Yahia
  • Y. Slimani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4081)


To avoid obtaining an unmanageable highly sized association rule sets– compounded with their low precision– that often make the perusal of knowledge ineffective, the extraction and exploitation of compact and informative generic basis of association rules is a becoming a must. Moreover, they provide a powerful verification technique for hampering gene mis-annotating or badly clustering in the Unigene library. However, extracted generic basis is still oversized and their exploitation is impractical. Thus, providing critical nuggets of extra-valued knowledge is a compellingly addressable issue. To tackle such a drawback, we propose in this paper a novel approach, called EGEA (Evolutionary Gene Extraction Approach). Such approach aims to considerably reduce the quantity of knowledge, extracted from a gene expression dataset, presented to an expert. Thus, we use a genetic algorithm to select the more predictive set of genes related to patient situations. Once, the relevant attributes (genes) have been selected, they serve as an input for a second approach stage, i.e., extracting generic association rules from this reduced set of genes. The notably decrease of the generic association rule cardinality, extracted from the selected gene set, permits to improve the quality of knowledge exploitation. Carried out experiments on a benchmark dataset pointed out that among this set, there are genes which are previously unknown prognosis-associated genes. This may serve as molecular targets for new therapeutic strategies to repress the relapse of pediatric acute myeloid leukemia (AML).


Generic association rules Genetic Algorithms Neural networks Frequent Closed itemset algorithms Bioinformatics 


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  1. 1.
    Wang, J., Zaki, M.J., Toivonen, H., Shasha, D.: Data Mining in Bioinformatics. Advanced Information and Knowledge Processing. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  2. 2.
    Chen, Y.: Bioinformatics Technologies. Advanced Information and Knowledge Processing. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)zbMATHCrossRefGoogle Scholar
  4. 4.
    Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Eengineering 15 (2003)Google Scholar
  5. 5.
    Cornuéjols, A., Miclet, L., Kodratoff, Y., Mitchell, T.: Apprentissage artificiel: concepts et algorithmes. Eyrolles (2002)Google Scholar
  6. 6.
    Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  7. 7.
    Trabelsi, A., Esseghir, M.A.: New evolutionary bankruptcy forecasting model based on genetic algorithms and neural networks. In: 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), pp. 241–245 (2005)Google Scholar
  8. 8.
    Liu, H., Li, J., Wong, L.: A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome Informatics 13, 51–60 (2002)Google Scholar
  9. 9.
    Shang, C., Shen, Q.: Aiding classification of gene expression data with feature selection: A comparative study. International Journal of Computational Intelligence Reasearch 1, 68–76 (2005)Google Scholar
  10. 10.
    Esseghir, M.A., Yahia, S.B., Abdelhak, S.: Localizing compact set of genes involved in cancer diseases using an evolutionary conectionist approach. In: European Conferences on Machine Learning and European Conferences on Principles and Practice of Knowledge Discovery in Databases. ECML/PKDD Discovery Challenge (2005)Google Scholar
  11. 11.
    Narayanan, A., Cheung, A., Gamalielsson, J., Keedwell, E., Vercellone, C.: Artificial neural networks for reducing the dimensionality of gene expression data. In: Bioinformatics Using Computational Intelligence Paradigms, vol. 176, pp. 191–211. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge (1986)Google Scholar
  13. 13.
    Zaki, M.J.: Mining non-redundant association rules. Data Mining Knowledge Discovery 9, 223–248 (2004)CrossRefMathSciNetGoogle Scholar
  14. 14.
    Gasmi, G., BenYahia, S., Nguifo, E.M., Slimani, Y.: IGB: A new informative generic base of association rules. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 81–90. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Kryszkiewicz, M.: Representative association rules and minimum condition maximum consequence association rules. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, pp. 361–369. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Zaki, M.: Mining Non-Redundant Association Rules. In: Data Mining and Knowledge Discovery, pp. 223–248 (2004)Google Scholar
  17. 17.
    Zaki, M.J.: Generating non-redundant association rules. In: Proceedings of the 6th ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, Massachusetts, USA, pp. 34–43 (2000)Google Scholar
  18. 18.
    Bastide, Y., Pasquier, N., Taouil, R., Lakhal, L., Stumme, G.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Palamidessi, C., Moniz Pereira, L., Lloyd, J.W., Dahl, V., Furbach, U., Kerber, M., Lau, K.-K., Sagiv, Y., Stuckey, P.J. (eds.) CL 2000. LNCS (LNAI), vol. 1861, pp. 972–986. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  19. 19.
    Pyle, D.: Data Preparation for Data Mining (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. A. Esseghir
    • 1
  • G. Gasmi
    • 1
  • S. Ben Yahia
    • 1
  • Y. Slimani
    • 1
  1. 1.Département des Sciences de l’InformatiqueFaculté des Sciences de TunisTunisTunisie

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