Iterated Local Search for Biclustering of Microarray Data

  • Wassim Ayadi
  • Mourad Elloumi
  • Jin-Kao Hao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6282)


In the context of microarray data analysis, biclustering aims to identify simultaneously a group of genes that are highly correlated across a group of experimental conditions. This paper presents a Biclustering Iterative Local Search (BILS) algorithm to the problem of biclustering of microarray data. The proposed algorithm is highlighted by the use of some original features including a new evaluation function, a dedicated neighborhood relation and a tailored perturbation strategy. The BILS algorithm is assessed on the well-known yeast cell-cycle dataset and compared with two most popular algorithms.


Analysis of DNA microarray data biclustering evaluation function iterative local search 


  1. 1.
    Aguilar-Ruiz, J.S.: Shifting and scaling patterns from gene expression data. Bioinformatics 21, 3840–3845 (2005)CrossRefPubMedGoogle Scholar
  2. 2.
    Ashburner, M., Ball, C.A., Blake, J.A., Bolstein, D., Butler, H., Cherry, M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., Harris, M.A., Hill, D.P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J.C., Richardson, J.E., Ringwald, M., Rubinand, G.M., Sherlock, G.: Gene ontology: tool for the unification of biology. the gene ontology consortium. Nature Genetics 25, 25–29 (2000)CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Ayadi, W., Elloumi, M.: Biclustering of Microarray Data. In: Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications. John Wiley & Sons Inc., Chichester (to appear 2010)Google Scholar
  4. 4.
    Ayadi, W., Elloumi, M., Hao, J.K.: A biclustering algorithm based on a bicluster enumeration tree: Application to dna microarray data. BioData Mining 2, 9 (2009)CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Barkow, S., Bleuler, S., Prelic, A., Zimmermann, P., Zitzler, E.: Bicat: a biclustering analysis toolbox. Bioinformatics 22(10), 1282–1283 (2006)CrossRefPubMedGoogle Scholar
  6. 6.
    Ben-Dor, A., Chor, B., Karp, R., Yakhini, Z.: Discovering local structure in gene expression data: the order-preserving submatrix problem. In: RECOMB ’02: Proceedings of the sixth annual international conference on Computational biology, pp. 49–57. ACM, New York (2002)CrossRefGoogle Scholar
  7. 7.
    Berriz, G.F., Beaver, J.E., Cenik, C., Tasan, M., Roth, F.P.: Next generation software for functional trend analysis. Bioinformatics 25(22), 3043–3044 (2009)CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Bryan, K., Cunningham, P., Bolshakova, N.: Application of simulated annealing to the biclustering of gene expression data. IEEE Transactions on Information Technology on Biomedicine 10(3), 519–525 (2006)CrossRefGoogle Scholar
  9. 9.
    Cheng, K.O., Law, N.F., Siu, W.C., Liew, A.W.: Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization. BMC Bioinformatics 9(210) (2008)Google Scholar
  10. 10.
    Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pp. 93–103. AAAI Press, Menlo Park (2000)Google Scholar
  11. 11.
    Cheng, Y., Church, G.M.: Biclustering of expression data. Technical report (supplementary information) (2006)Google Scholar
  12. 12.
    Das, S., Idicula, S.M.: Application of reactive grasp to the biclustering of gene expression data. In: ISB ’10: Proceedings of the International Symposium on Biocomputing, pp. 1–8. ACM, New York (2010)Google Scholar
  13. 13.
    Dharan, A., Nair, A.S.: Biclustering of gene expression data using reactive greedy randomized adaptive search procedure. BMC Bioinformatics 10(suppl. 1), S27 (2009)CrossRefGoogle Scholar
  14. 14.
    Divina, F., Aguilar-Ruiz, J.S.: Biclustering of expression data with evolutionary computation. IEEE Transactions on Knowledge and Data Engineering 18(5), 590–602 (2006)CrossRefGoogle Scholar
  15. 15.
    Divina, F., Aguilar-Ruiz, J.S.: A multi-objective approach to discover biclusters in microarray data. In: GECCO ’07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 385–392. ACM, New York (2007)CrossRefGoogle Scholar
  16. 16.
    Gallo, C.A., Carballido, J.A., Ponzoni, I.: Microarray biclustering: A novel memetic approach based on the pisa platform. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 44–55. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Hartigan, J.A.: Direct clustering of a data matrix. Journal of the American Statistical Association 67(337), 123–129 (1972)CrossRefGoogle Scholar
  18. 18.
    Hoos, H., Stutzle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2004)Google Scholar
  19. 19.
    Lourenco, H.R., Martin, O., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Meta-heuristics, pp. 321–353. Springer, Heidelberg (2003)Google Scholar
  20. 20.
    Liu, J., Wang, W.: Op-cluster: Clustering by tendency in high dimensional space. In: IEEE International Conference on Data Mining, pp. 187–194 (2003)Google Scholar
  21. 21.
    Luan, Y., Li, H.: Clustering of time-course gene expression data using a mixed-effects model with B-splines. Bioinformatics 19(4), 474–482 (2003)CrossRefPubMedGoogle Scholar
  22. 22.
    Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 1(1), 24–45 (2004)CrossRefGoogle Scholar
  23. 23.
    Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recogn. 39(12), 2464–2477 (2006)CrossRefGoogle Scholar
  24. 24.
    Peddada, S.D., Lobenhofer, E.K., Li, L., Afshari, C.A., Weinberg, C.R., Umbach, D.M.: Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference. Bioinformatics 19(7), 834–841 (2003)CrossRefPubMedGoogle Scholar
  25. 25.
    Pontes, B., Divina, F., Giráldez, R., Aguilar-Ruiz, J.S.: Virtual error: A new measure for evolutionary biclustering. In: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, pp. 217–226 (2007)Google Scholar
  26. 26.
    Prelic, A., Bleuler, S., Zimmermann, P., Buhlmann, P., Gruissem, W., Hennig, L., Thiele, L., Zitzler, E.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)CrossRefPubMedGoogle Scholar
  27. 27.
    Schliep, A., Schonhuth, A., Steinhoff, C.: Using hidden Markov models to analyze gene expression time course data. Bioinformatics 19(Suppl. 1), i255–i263 (2003)CrossRefGoogle Scholar
  28. 28.
    Tavazoie, S., Hughes, J.D., Campbell, M.J., Cho, R.J., Church, G.M.: Systematic determination of genetic network architecture. Nature Genetics 22, 281–285 (1999)CrossRefPubMedGoogle Scholar
  29. 29.
    Teng, L., Chan, L.: Discovering biclusters by iteratively sorting with weighted correlation coefficient in gene expression data. J. Signal Process. Syst. 50(3), 267–280 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Wassim Ayadi
    • 1
    • 2
  • Mourad Elloumi
    • 2
  • Jin-Kao Hao
    • 1
  1. 1.LERIAUniversity of AngersAngersFrance
  2. 2.UTICHigher School of Sciences and Technologies of TunisTunisTunisia

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