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Shifting Patterns Discovery in Microarrays with Evolutionary Algorithms

  • Beatriz Pontes
  • Raúl Giráldez
  • Jesús S. Aguilar–Ruiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)

Abstract

In recent years, the interest in extracting useful knowledge from gene expression data has experimented an enormous increase with the development of microarray technique. Biclustering is a recent technique that aims at extracting a subset of genes that show a similar behaviour for a subset conditions. It is important, therefore, to measure the quality of a bicluster, and a way to do that would be checking if each data submatrix follows a specific trend, represented by a pattern. In this work, we present an evolutionary algorithm for finding significant shifting patterns which depict the general behaviour within each bicluster. The empirical results we have obtained confirm the quality of our proposal, obtaining very accurate solutions for the biclusters used.

Keywords

Gene Expression Data Biclustering Evolutionary Algorithm Shifting Pattern 

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References

  1. 1.
    Aguilar-Ruiz, J.S.: Shifting and scaling patterns from gene expression data. Bioinformatics 21, 3840–3845 (2005)CrossRefGoogle Scholar
  2. 2.
    Aguilar-Ruiz, J.S., Divina, F.: Biclustering of expression data with evolutionary computation. IEEE Transactions on Knowledge & Data Engineering (to be published)Google Scholar
  3. 3.
    Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson, J., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O., Staudt, L.M.: Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)CrossRefGoogle Scholar
  4. 4.
    Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering gene expression patterns. Journal of Computational Biology 6(3-4), 281–297 (1999)CrossRefGoogle Scholar
  5. 5.
    Bleuler, S., Prelić, A., Zitzler, E.: An ea framework for biclustering of gene expression data, Piscataway, NJ, pp. 166–173 (2000)Google Scholar
  6. 6.
    Bryan, K., Cunningham, P., Bolshakova, N.: Biclustering of expression data using simulated annealing. In: 18th IEEE Symposium on Computer-Based Medical Systems, Dublin, Ireland, pp. 383–388 (2005)Google Scholar
  7. 7.
    Chambers, L.D., et al.: Practical Handbook of Genetic Algorithms, vol. III. CRC Press, Boca Raton (1999)zbMATHGoogle Scholar
  8. 8.
    Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the 8th International Conference on Intellingent Systemns for Molecular Biology, La Jolla, CA, pp. 93–103 (2000)Google Scholar
  9. 9.
    Cho, R., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Gabrielian, A., Landsman, D., Lockhart, D., Davis, R.: A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2, 65–73 (1998)CrossRefGoogle Scholar
  10. 10.
    DeJong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan (1975)Google Scholar
  11. 11.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  12. 12.
    Hartigan, J.A.: Direct clustering of a data matrix. Journal of the American Statistical Association 67(337), 123–129 (1972)CrossRefGoogle Scholar
  13. 13.
    Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics 1, 24–25 (2004)CrossRefGoogle Scholar
  14. 14.
    Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18, 136–144 (2002)Google Scholar
  15. 15.
    Wang, H., Wang, W., Yang, J., Yu, P.S.: Clustering by pattern similarity in large data sets. In: ACM SIGMOD International Conference on Management of Data, Madison, WI, pp. 394–405 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Beatriz Pontes
    • 1
  • Raúl Giráldez
    • 2
  • Jesús S. Aguilar–Ruiz
    • 2
  1. 1.Department of Computer ScienceUniversity of SevilleSevillaSpain
  2. 2.Area of Computer ScienceUniversity of Pablo de OlavideSevillaSpain

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