Advertisement

Obtaining Biclusters in Microarrays with Population-Based Heuristics

  • Pablo Palacios
  • David Pelta
  • Armando Blanco
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3907)

Abstract

In this article, we shall analyze the behavior of population-based heuristics for obtaining biclusters from DNA microarray data. More specifically, we shall propose an evolutionary algorithm, an estimation of distribution algorithm, and several memetic algorithms that differ in the local search used.

In order to analyze the effectiveness of the proposed algorithms, the freely available yeast microarray dataset has been used. The results obtained have been compared with the algorithm proposed by Cheng and Church.

Both in terms of the computation time and the quality of the solutions, the comparison reveals that a standard evolutionary algorithm and the estimation of distribution algorithm offer an efficient alternative for obtaining biclusters.

Keywords

Local Search Taboo Search Memetic Algorithm Distribution Algorithm Local Search Scheme 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baluja, S., Caruana, R.: Removing the genetics from the standard genetic algorithm. In: Prieditis, A., Russel, S. (eds.) The Int. Conf. on Machine Learning, San Mateo, pp. 38–46. Morgan Kaufmann Publishers, CA (1995)Google Scholar
  2. 2.
    Busygin, S., Jacobsen, G., Kramer, E.: Double conjugated clustering applied to leukemia microarray data. In: SIAM ICDM, Workshop on clustering high dimensional (2002)Google Scholar
  3. 3.
    Cheng, Y., Church, G.: Biclustering of expression data. In: 8th International Conference on Intelligent System for Molecular Biology, pp. 93–103 (2001)Google Scholar
  4. 4.
    Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE-EC 3(4), 287 (1999)Google Scholar
  5. 5.
    Hart, W., Krasnogor, N., Smith, J.: Recent Advances in Memetic Algorithms. In: Studies in Fuzziness and Soft Computing. Physica-Verlag (2004)Google Scholar
  6. 6.
    Hartigan, J.: Clustering Algorithms. John Wiley, New York (1975)MATHGoogle Scholar
  7. 7.
    Madeira, S., Olivera, A.: Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on computational biology an bioinformatics 1(1), 24–45 (2004)CrossRefGoogle Scholar
  8. 8.
    Muehlenbein, H., Paab, G.: From recombination of genes to the estimation of distributions. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  9. 9.
    Muhlenbein, H.: Evolutionary computation: The equation for response to selection and its use for prediction. Evolutionary Computation (5), 303–346 (1998)Google Scholar
  10. 10.
    Nagesh, H., Goil, S., Choudhary, A.: pmafia: A scalable parallel subspace clustering algorithm for massive data sets. In: International Conference on Parallel Processing, p. 477 (2000)Google Scholar
  11. 11.
    Ruiz, J.A.: Shifting and scaling patterns from gene expression data. Bioinformatics 21(20), 3840–3845 (2005)CrossRefGoogle Scholar
  12. 12.
    Schikuta, E.: Grid-clustering: An efficient hierarchical clustering method for very large data sets. In: Proc.13th Int. Conf. Pattern Recognition, vol. 2, pp. 101–105. IEEE Computer Society, Los Alamitos (1996)CrossRefGoogle Scholar
  13. 13.
    Sharan, R., Shamir, R.: Click: A clustering algorithm with applications to gene expression analysis. In: Proceedings of the Eighth International Conference on Intelligent Systems, pp. 307–316 (2000)Google Scholar
  14. 14.
    Wang, H., Wang, W., Yang, J., Yu, P.: Clustering by pattern similarity in large data sets. In: SIGMOD Conference (2002)Google Scholar
  15. 15.
    Wang, W., Yang, J., Muntz, R.: Sting: A statistical information grid approach to spatial data mining. In: Proc. 23rd Conf. Very Large Databases, pp. 186–195 (1997)Google Scholar
  16. 16.
    Yang, J., Wang, H., Wang, W., Yu, P.: Improving performance of bicluster discovery in a large data set. In: Proceedings of the 6th ACM International Conference on Research in Computational Molecular Biology (RECOMB) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pablo Palacios
    • 1
  • David Pelta
    • 2
  • Armando Blanco
    • 2
  1. 1.Dept. de Ingeniería Electrónica, Sistemas Informáticos y AutomáticaUniversidad de HuelvaHuelva
  2. 2.Depto. de Ciencias de la Computación e I.A.Universidad de GranadaGranadaSpain

Personalised recommendations