Actas de congresos
New Perspectives For The Biclustering Problem
Registro en:
0780394879; 9780780394872
2006 Ieee Congress On Evolutionary Computation, Cec 2006. , v. , n. , p. 753 - 760, 2006.
2-s2.0-34547340995
Autor
De Franca F.O.
Bezerra G.
Von Zuben F.J.
Institución
Resumen
Multimodal optimization algorithms inspired by the immune system are generally characterized by a dynamic control of the population size and by diversity maintenance along the search. One of these proposals, denoted copt-aiNet (artificial immune network for combinatorial optimization), is used to deal with combinatorial problems like the Traveling Salesman Problem (TSP) and other permutation problems. In this paper, the copt-aiNet algorithm is extended and adapted to be applied to an important issue of modern data mining, the biclustering problem. The biclustering approach consists in simultaneously ordering the rows and columns of a given matrix, so that similar elements are grouped together. To illustrate the performance of the proposed method, two bitmap images are scrambled and used as input to the algorithm, and the biclustering procedure tries to restore the original image by grouping the pixels according to the similarity of colors in a neighborhood. Additionally, copt-aiNet is applied to gene expression data clustering, a classical problem of the bioinformatics literature, and its performance is compared with a hierarchical biclustering algorithm. © 2006 IEEE.
753 760 Agrawal, R., Gehrke, J., Gunopulus, D., Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications (1998) Proceedings of the ACM/SIGMOD International Conference on Management of Data, pp. 94-105 Y. Cheng and G. M. Church. Biclustering of expression data, In Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology (ISMB '00), 2000, pp. 93-103de Castro, L.N., Timmis, J., An Artificial Immune Network for Multimodal Function Optimization (2002) Proceedings of the IEEE Congress on Evolutionary Computation, 1, pp. 699-674 de Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer-Verlag L. N. de Castro and F. J. Von Zuben. aiNet: An Artificial Immune Network for Data Analysis, In Data Mining: A Heuristic Approach, H. A. Abbass, R. A. Sarker, and C. S. Newton (Eds.), Idea Group Publishing, USA, Chapter XII, 2001, pp. 231-259de Castro, L.N., Von Zuben, F.J., Immune and Neural Network Models: Theoretical and Empirical Comparisons (2001) International Journal of Computational Intelligence and Applications (IJCIA), 3 (1), pp. 239-257 de Castro, L.N., Von Zuben, F.J., Learning and Optimization Using the Clonal Selection Principle (2002) IEEE Transactions on Evolutionary Computation, 3 (6), pp. 239-251 de França, F.O., Bio-Inspired Algorithms applied to Dynamic Optimization (2005) FEEC/Unicamp, , December, Master Thesis, School of Electrical and Computer Engineering, State University of Campinas, 139 p, in Portuguese de França, F.O., de Castro, L.N., Von Zuben, F.J., An Artificial Immune Network for Multimodal Function Optimization on Dynamic Environments (2005) Proceedings of the Genetic and Evolutionary Computation Conference, pp. 289-296 de Sousa, J.S., Gomes, L.C.T., Bezerra, G.B., de Castro, L.N., Von Zuben, F.J., An Immune-Evolutionary Algorithm for Multiple Rearrangements of Gene Expression Data (2004) Genetic Programming and Evolvable Machines, 5 (2), pp. 157-179 Dhillon, I.S., Co-clustering documents and words using bipartite spectral graph partitioning (2001) Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'01), pp. 269-274 Eisen, M.B., Spellman, P.T., Brow, P.O., Botstein, D., Cluster Analysis and Display of Genome-wide Expression Patterns (1998) Proc. Natl. Acad. Sci, USA, 95, pp. 14863-14868 Everitt, B.S., Landau, S., Leese, M., (2001) Cluster Analysis, , Edward Arnold, London Getz, G., Levine, E., Domany, E., Coupled two-way clustering analysis of gene microarray data (2000) In Proceedings of the Natural Academy of Sciences USA, pp. 12079-12084 Glover, F.W., Kochenberger, G.A., (2002) Handbook of Metaheuristics, , Kluwer Academic Publishers Hartigan, J.A., Direct clustering of a data matrix (1972) Journal of the American Statistical Association (JASA), 67 (337), pp. 123-129 Haixun, W., Wei, W., Jiong, Y., Yu, P.S., Clustering by pattern similarity in large data sets (2002) Proceedings of the 2002 ACM SIGMOD Int. Conf. on Management of Data, pp. 394-405 Hartigan, J.A., Direct clustering of a data matrix (1972) Journal of the American Statistical Association (JASA), 67 (337), pp. 123-129 Jerne, N.K., Towards a Network Theory of the Immune System Ann. Immunol, (Cand 1974), pp. 373-389. , Inst. Pasteur, 1.25 Jiong, Y., Wei, W., Haixun, W., Philip, Y., Æ-clusters: Capturing subspace correlation in a large data set (2002) Proceedings of the 18th IEEE Int. Conference on Data Engineering, pp. 517-528 M. Kapushesky, P. Kemmeren, A.C. Culhane, S. Durinck, J. Ihmels, C. Körner, M. Kull, A. Torrente, U. Sarkans, J. Vilo and A. Brazma. Expression Profiler: next generation - an online platform for analysis of microarray data. Nucleic Acids Research, 2004, 32 (Web Server issue):W465-W470Liu, J., Wang, W., Op-cluster: Clustering by tendency in high dimensional space (2003) Proceedings of the 3rd IEEE International Conference on Data Mining, pp. 187-194 Madeira, S.C., Oliveira, A.L., Biclustering algorithms for biological data analysis: A survey (2004) IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1 (1), pp. 24-45 Mahfoud, S.W., (1995) Niching Methods for Genetic Algorithms, , Illinois Genetic Algorithms Laboratory, Illinois, IL, IlliGAL Report no. 95001, May Mendes, A., Cotta, C., Garcia, V., Franca, P., Moscato, P., Parallel Memetic Algorithms for Gene Ordering in Microarray Data (2005) Proceedings of the 1st Worshop on Parallel Bioinspired Algorithms, pp. 604-611. , IEEE Computer Society Press, Oslo, Norway, June Moscato, P., Berretta, R., Mendes, A., A New Memetic Algorithm for Ordering Datasets: Applications in Microarray Analysis (2005) Proc. of the MIC2005 - The 6th Metaheuristics International Conference, pp. 695-700. , K.F. Doerner et al, eds, Vienna, Austria, August Oprea, M., Antibody Repertoires and Pathogen Recognition: The Role of Germline Diversity and Somatic Hypermutation (1999), Ph.D. Dissertation, University of New Mexico, EUAPerelson, A.S., Immune Network Theory (1989) Imm. Rev, (110), pp. 5-36 Segal, E., Taskar, B., Gasch, A., Friedman, N., Koller, D., Rich probabilistic models for gene expression (2001) In Bioinformatics, 17 (SUPPL. 1), pp. S243-S252 Q. Sheng, Y. Moreau, and B. De Moor. Biclustering micrarray data by Gibbs sampling. In Bioinformatics, 2003, 19 (Suppl. 2), pp. ii196-ii205Storb, U., Progrese in Understanding the Mechanisms and Consequences of Somatic Hypermutation (1998) Immun. Rev, (162), pp. 5-11 Tang, C., Zhang, L., Zhang, I., Ramanathan, M., Interrelated two-way clustering: An unsupervised approach for gene expression data analysis (2001) Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering, pp. 41-48 Whitley, D., Rana, S., Heckendorn, R.B., Island Model Genetic Algorithms and Linearly Separable Problems (1997) Lecture Notes in Computer Science, 1305, pp. 109-125. , Proceedings of the AISB Workshop on Evolutionary Computation, D. Corne and J. L. Shapiro Eds