Applying GCS Networks to Fuzzy Discretized Microarray Data for Tumour Diagnosis

  • Fernando Díaz
  • Florentino Fdez-Riverola
  • Daniel Glez-Peña
  • J. M. Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Gene expression profiles belonging to DNA microarrays are composed of thousands of genes at the same time, representing the complex relationships between them. In this context, the ability of designing methods capable of overcoming current limitations is crucial to reduce the generalization error of state-of-the-art algorithms. This paper presents the application of a self-organised growing cell structures network in an attempt to cluster biological homogeneous patients. This technique makes use of a previous successful supervised fuzzy pattern algorithm capable of performing DNA microarray data reduction. The proposed model has been tested with microarray data belonging to bone marrow samples from 43 adult patients with cancer plus a group of six cases corresponding to healthy persons. The results of this work demonstrate that classical artificial intelligence techniques can be effectively used for tumour diagnosis working with high-dimensional microarray data.


Acute Myeloid Leukemia Microarray Data Weight Vector Bone Marrow Sample Tumour Diagnosis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Piatetsky-Shapiro, G., Tamayo, P.: Microarray data mining: facing the challenges. ACM SIGKDD Explorations Newsletter 5(2), 1–5 (2003)CrossRefGoogle Scholar
  2. Cho, S.B., Won, H.H.: Machine learning in DNA microarray analysis for cancer classification. In: Proc. of the First Asia-Pacific Bioinformatics Conference, pp. 189–198 (2003)Google Scholar
  3. Ochs, M.F., Godwin, A.K.: Microarrays in Cancer: Research and Applications. BioTechniques 15, 14–15 (2003)Google Scholar
  4. Xiang, Z.Y., Yang, Y., Ma, X., Ding, W.: Microarray expression profiling: Analysis and applications. Current Opinion in Drug Discovery & Development 6(3), 384–395 (2003)Google Scholar
  5. Golub, T.: Genome-Wide Views of Cancer. The New England Journal of Medicine 344, 601–602 (2001)CrossRefGoogle Scholar
  6. Cakmakov, D., Bennani, Y.: Feature selection for pattern recognition. Informa Press (2003)Google Scholar
  7. Díaz, F., Fdez-Riverola, F., Corchado, J.M.: GENE-CBR: a Case-Based Reasoning Tool for Cancer Diagnosis using Microarray Datasets. Computational Intelligence (in Press) ISSN 0824-7935Google Scholar
  8. Fdez-Riverola, F., Díaz, F., Borrajo, M.L., Yáñez, J.C., Corchado, J.M.: Improving Gene Selection in Microarray Data Analysis using fuzzy Patterns inside a CBR System. In: Proc. Of the 6th International Conference on Case-Based Reasoning, pp. 191–205 (2005)Google Scholar
  9. Fritzke, B.: Growing Self-organising Networks – Why? In: Proc. of the European Symposium on Artificial Neural Networks, pp. 61–72 (1993)Google Scholar
  10. Kohonen, T.: Self-Organising Maps. Springer, Heidelberg (1995)Google Scholar
  11. Fritzke, B.: Growing Cell Structures - A Self-organizing Network for Unsupervised and Supervised Learning. Technical Report, International Computer Science Institute, Berkeley (1993)Google Scholar
  12. Vardiman, W., Harris, N.L., Brunning, R.D.: The World Health Organization (WHO) classification of the myeloid neoplasms. Blood 100, 2292–2302 (2002)CrossRefGoogle Scholar
  13. Grimwade, D., Walker, H., Oliver, F., Wheatley, K., Harrison, C., Harrison, G., Rees, J., Hann, I., Stevens, R., Burnett, A., Goldstone, A.: The importance of diagnostic cytogenetics on outcome in AML: analysis of 1,612 patients entered into the MRC AML 10 trial. Blood 92, 2322–2333 (1998)Google Scholar
  14. Slovak, M.L., Kopecky, K.J., Cassileth, P.A., Harrington, D.H., Theil, K.S., Mohamed, A., Paietta, E., Willman, C.L., Head, D.R., Rowe, J.M., Forman, S.J., Appelbaum, F.R.: Karyotypic analysis predicts outcome of preremission and postremission therapy in adult acute myeloid leukemia: a Southwest Oncology Group/Eastern Cooperative Oncology Group Study. Blood 96, 4075–4083 (2000)Google Scholar
  15. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fernando Díaz
    • 1
  • Florentino Fdez-Riverola
    • 2
  • Daniel Glez-Peña
    • 2
  • J. M. Corchado
    • 3
  1. 1.Dept. InformáticaUniversity of Valladolid, Escuela Universitaria de InformáticaSegoviaSpain
  2. 2.Dept. InformáticaUniversity of Vigo, Escuela Superior de Ingeniería Informática, Edificio PolitécnicoOurenseSpain
  3. 3.Dept. Informática y AutomáticaUniversity of SalamancaSalamancaSpain

Personalised recommendations