Abstract
In this paper, a new memetic approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with local search for microarray biclustering is presented. The original features of this proposal are the consideration of opposite regulation and incorporation of a mechanism for tuning the balance between the size and row variance of the biclusters. The approach was developed according to the Platform and Programming Language Independent Interface for Search Algorithms (PISA) framework, thus achieving the possibility of testing and comparing several different memetic MOEAs. The performance of the MOEA strategy based on the SPEA2 performed better, and its resulting biclusters were compared with those obtained by a multi-objective approach recently published. The benchmarks were two datasets corresponding to Saccharomyces cerevisiae and human B-cells Lymphoma. Our proposal achieves a better proportion of coverage of the gene expression data matrix, and it also obtains biclusters with new features that the former existing evolutionary strategies can not detect.
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References
Madeira, S., Oliveira, A.L.: Biclustering Algorithms for Biological Data Analysis: A Survey. IEEE-ACM Trans. Comput. Biol. Bioinform. 1, 24–45 (2004)
Cheng, Y., Church, G.M.: Biclustering of Expression Data. In: Proceedings of the 8th International Conf. on Intelligent Systems for Molecular Biology, pp. 93–103 (2000)
Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recognit. 39, 2464–2477 (2006)
Divina, F., Aguilar-Ruiz, J.S.: Biclustering of Expression Data with Evolutionary Computation. IEEE Trans. Knowl. Data Eng. 18(5), 590–602 (2006)
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)
Cho, R., et al.: A genome-wide transcriptional analysis of the mitotic cell cycle. Mol. Cell 2(1), 65–73 (1998)
Menges, M., Hennig, L., Gruissem, W., Murray, J.: Genome-wide gene expression in an Arabidopsis cell suspension. Plant Mol. Biol. 53(4), 423–442 (2003)
Laule, O., et al.: Crosstalk between cytosolic and plastidial pathways of isoprenoid bio systhesis in arabidopsis thaliana. PNAS 100(11), 6866–6871 (2003)
Alizadeh, A.A., et al.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)
Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA - A Platform and Programming Language Independent Interface for Search Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 494–508. Springer, Heidelberg (2003)
Zitzler, E., Künzli, S.: Indicator-Based Selection in Multiobjective Search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)
Deb, K., Agraval, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
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)
Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. TIK Computer Engineering and Networks Laboratory (2005)
Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C., Grunert da Fonseca, V.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)
Hansen, M., Jaszkiewicz, A.: Evaluating the quality of approximations to the non-dominated set. Technical University of Denmark (1998)
Conover, W.: Practical Nonparametric Statistics. John Wiley & Sons, New York (1999)
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Gallo, C.A., Carballido, J.A., Ponzoni, I. (2009). Microarray Biclustering: A Novel Memetic Approach Based on the PISA Platform. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2009. Lecture Notes in Computer Science, vol 5483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01184-9_5
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DOI: https://doi.org/10.1007/978-3-642-01184-9_5
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