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Applying CBR Systems to Micro Array Data Classification

  • Sara Rodríguez
  • Juan F. De Paz
  • Javier Bajo
  • Juan M. Corchado
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 49)

Summary

Microarray technology allows to measureing the expression levels of thousands of genes in an experiment. This technology required requires computational solutions capable of dealing with great amounts of data and as well as techniques to explore the data and extract knowledge which allow patients classification. This paper presents a systems based on Case-based reasoning (CBR) for automatic classification of leukemia patients from microarray data. The system incorporates novel algorithms for data mining that allow to filter and classify as well as extraction of knowledge. The system has been tested and the results obtained are presented in this paper.

Keywords

Case-based Reasoning HG U133 dendogram leukemia classification decision tree 

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References

  1. 1.
    Shortliffe, E., Cimino, J.: Biomedical Informatics: Computer Applications in Health Care and Biomedicine. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Tsoka, S., Ouzounis, C.: Recent developments and future directions in computational genomics. FEBS Letters 480(1), 42–48 (2000)CrossRefGoogle Scholar
  3. 3.
    Lander, E., et al.: Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001)CrossRefGoogle Scholar
  4. 4.
    Rubnitz, J., Hijiya, N., Zhou, Y., Hancock, M., Rivera, G., Pui, C.: Lack of benefit of early detection of relapse after completion of therapy for acute lymphoblastic leukemia. Pediatric Blood & Cancer 44(2), 138–141 (2005)CrossRefGoogle Scholar
  5. 5.
    Armstrong, N., van de Wiel, M.: Microarray data analysis: From hypotheses to conclusions using gene expression data. Cellular Oncology 26(5-6), 279–290 (2004)Google Scholar
  6. 6.
    Quackenbush, J.: Computational analysis of microarray data. Nature Review Genetics 2(6), 418–427 (2001)CrossRefGoogle Scholar
  7. 7.
    Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)Google Scholar
  8. 8.
    Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K., Scherf, U., Speed, T.: Exploration, Normalization, and Summaries of High density Oligonucleotide Array Probe Level Data. Biostatistics 4, 249–264 (2003)zbMATHCrossRefGoogle Scholar
  9. 9.
    Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics, 59–69 (1982)Google Scholar
  10. 10.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D., Leen, T. (eds.), Advances in Neural Information Processing Systems, vol. 7, pp. 625–632, Cambridge (1995)Google Scholar
  11. 11.
    Martinetz, T.: Competitive Hebbian learning rule forms perfectly topology preserving maps. In: ICANN 1993: International Conference on Artificial Neural Networks, pp. 427–434. Springer, Heidelberg (1993)Google Scholar
  12. 12.
    Martinetz, T., Schulten, K.: A neural-gas network learns topologies. In: Kohonen, T., Makisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, Amsterdam, pp. 397–402 (1991)Google Scholar
  13. 13.
    Brunelli, R.: Histogram Analysis for Image Retrieval. Pattern Recognition 34, 1625–1637 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Jolliffe, I.: Principal Component Analysis, 2nd edn. Series in Statistics. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  15. 15.
    Riverola, F., Daz, F., Corchado, J.: Gene-CBR: a case-based reasoning tool for cancer diagnosis using microarray datasets. Computational Intelligence 22(3-4), 254–268 (2006)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)Google Scholar
  17. 17.
    Saitou, N., Nie, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4, 406–425 (1987)Google Scholar
  18. 18.
    Sneath, P., Sokal, R.: Numerical Taxonomy. The Principles and Practice of Numerical Classification. W.H. Freeman Company, San Francisco (1973)zbMATHGoogle Scholar
  19. 19.
    Breiman, L., Friedman, J., Olshen, A., Stone, C.: Classification and regression trees. Wadsworth International Group, Belmont (1984)zbMATHGoogle Scholar
  20. 20.
    Quinlan, J.: Discovering rules by induction from large collections of examples. In: Michie, D. (ed.) Expert systems in the micro electronic age, pp. 168–201. Edinburgh University Press, Edinburgh (1979)Google Scholar
  21. 21.
    Holder, D., Raubertas, R., Pikounis, V., Svetnik, V., Soper, K.: Statistical analysis of high density oligonucleotide arrays: a SAFER approach. In: Proceedings of the ASA Annual Meeting Atlanta, GA (2001)Google Scholar
  22. 22.
    Corchado, J., Corchado, E., Aiken, J., Fyfe, C., Fdez-Riverola, F., Glez-Bedia, M.: Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems. In: Proceedings. of the 5th International Conference on Case-Based Reasoning, pp. 107–121 (2003)Google Scholar
  23. 23.
    Quackenbush, J.: Microarray Analysis and Tumor Classification. The new england journal o f medicine, 2463–2472 (2006)Google Scholar
  24. 24.
    Zhenyu, C., Jianping, L., Liwei, W.: A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue. Artificial Intelligence in Medicine 41, 161–175 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sara Rodríguez
    • 1
  • Juan F. De Paz
    • 1
  • Javier Bajo
    • 1
  • Juan M. Corchado
    • 1
  1. 1.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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