BiHEA: A Hybrid Evolutionary Approach for Microarray Biclustering

  • Cristian Andrés Gallo
  • Jessica Andrea Carballido
  • Ignacio Ponzoni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5676)


In this paper a new hybrid approach that integrates an evolutionary algorithm with local search for microarray biclustering is presented. The novelty of this proposal is constituted by the incorporation of two mechanisms: the first one avoids loss of good solutions through generations and overcomes the high degree of overlap in the final population; and the other one preserves an adequate level of genotypic diversity. The performance of the memetic strategy was compared with the results of several salient biclustering algorithms over synthetic data with different overlap degrees and noise levels. In this regard, our proposal achieves results that outperform the ones obtained by the referential methods. Finally, a study on real data was performed in order to demonstrate the biological relevance of the results of our approach.


gene expression data biclustering evolutionary algorithms 


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  1. 1.
    Madeira, S., Oliveira, A.L.: Biclustering Algorithms for Biological Data Analysis: A Survey. IEEE-ACM Trans. Comput. Biol. Bioinform. 1, 24–45 (2004)CrossRefGoogle Scholar
  2. 2.
    Cheng, Y., Church, G.M.: Biclustering of Expression Data. In: Proceedings of the 8th Inter-national Conf. on Intelligent Systems for Molecular Biology, pp. 93–103 (2000)Google Scholar
  3. 3.
    Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recognit. 39, 2464–2477 (2006)CrossRefGoogle Scholar
  4. 4.
    Divina, F., Aguilar-Ruiz, J.S.: Biclustering of Expression Data with Evolutionary Computation. IEEE Trans. Knowl. Data Eng. 18(5), 590–602 (2006)CrossRefGoogle Scholar
  5. 5.
    Bleuler, S., Prelic, A., Zitzler, E.: An EA framework for biclustering of gene expression data. In: Proceeding of Congress on Evolutionary Computation, pp. 166–173 (2004)Google Scholar
  6. 6.
    Ihmels, J., Bergmann, S., Barkai, N.: Defining transcription modules using large-scale gene expression data. Bioinformatics 20(13), 1993–2003 (2004)CrossRefPubMedGoogle Scholar
  7. 7.
    Zimmermann, P., Wille, A., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E., Prelic, A., Bleuler, S.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)CrossRefPubMedGoogle Scholar
  8. 8.
    Ben-Dor, A., Chor, B., Karp, R., Yakhini, Z.: Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem. In: Proc. Sixth Int’l Conf. Computational Biology (RECOMB 2002), pp. 49–57 (2002)Google Scholar
  9. 9.
    Gallo, C., Carballido, J.A., Ponzoni, I.: Microarray Biclustering: A Novel Memetic Approach Based on the PISA Platform. LNCS, vol. 5483, pp. 44–55. Springer, Heidelberg (2009)Google Scholar
  10. 10.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, Tsahalis, Periaux, Papailiou, Fogarty (eds.) Evolutionary Methods for Design, Optimisations and Control, pp. 19–26 (2002)Google Scholar
  11. 11.
    Tanay, A., et al.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18(suppl. 1), S136–S144 (2002)CrossRefGoogle Scholar
  12. 12.
    Draghici, S., Khatri, P., Bhavsar, P., Shah, A., Krawetz, S., Tainsky, M.: Onto-Tools, the toolkit of the modern biologist: Onto-Express, Onto-Compare, Onto-Design, and Onto-Translate. Nuc. Acids Res. 31(13), 3775–3781 (2003)CrossRefGoogle Scholar
  13. 13.
    Ihmels, J., et al.: Revealing modular organization in the yeast transcriptional network. Nat. Genet. 31, 370–377 (2002)PubMedGoogle Scholar
  14. 14.
    Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: BicAT: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)CrossRefPubMedGoogle Scholar
  15. 15.
    Alon, U., Barkai, N., Notterman, D., Gish, K., Ybarra, S., Mack, D., Levine, A.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96, 6745–6750 (1999)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cristian Andrés Gallo
    • 1
  • Jessica Andrea Carballido
    • 1
  • Ignacio Ponzoni
    • 1
    • 2
  1. 1.Laboratorio de Investigación y Desarrollo en Computación Científica (LIDeCC), Departamento de Ciencias e Ingeniería de la ComputaciónUniversidad Nacional del SurBahía BlancaArgentina
  2. 2.Planta Piloto de Ingeniería Química (PLAPIQUI) - UNS – CONICET, Complejo CRIBABBBahía BlancaArgentina

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