Abstract
Biclustering or simultaneous clustering attempts to find maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a range of conditions. The possibilistic approach extracts one bicluster at a time, by assigning to it a membership for each gene-condition pair. In this study, a novel evolutionary framework is introduced for generating optimal fuzzy possibilistic biclusters from microarray gene expression data. The different parameters controlling the size of the biclusters are tuned. The experimental results on benchmark datasets demonstrate better performance as compared to existing algorithms available in literature.
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References
Cheng, Y., Church, G.M.: Biclustering of gene expression data. In: Proceedings of ISMB 2000, pp. 93–103 (2000)
Pattern Recognition (Special Issue on Bioinformatics) 39 (2006)
Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: A survey. IEEE Transactions on Computational Biology and Bioinformatics 1, 24–45 (2004)
Getz, G., et al.: Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data. Bioinformatics 19, 1079–1089 (2003)
Hartigan, J.A.: Direct clustering of a data matrix. Journal of American Statistical Association 67(337), 123–129 (1972)
Yang, J., et al.: Enhanced biclustering on expression data. In: Proceedings of the Third IEEE Symposium on BioInformatics and Bioengineering (BIBE’03), pp. 1–7 (2003)
Tanay, A., Sharan, R., Shamir, R.: Discovering statistically significant biclusters in gene expression data. Bioinformatics 18, S136–S144 (2002)
Lazzeroni, L., Owen, A.: Plaid models for gene expression data. Statistica Sinica 12, 61–86 (2002)
Bleuler, S., Prelić, A., Zitzler, E.: An EA framework for biclustering of gene expression data. In: Proceedings of Congress on Evolutionary Computation, pp. 166–173 (2004)
Divina, F., Aguilar-Ruiz, J.S.: Biclustering of expression data with evolutionary computation. IEEE Transactions on Knowledge and Data Engineering 18, 590–602 (2006)
Filippone, M., et al.: Possibilistic approach to biclustering: An application to oligonucleotide microarray data analysis. In: Priami, C. (ed.) CMSB 2006. LNCS (LNBI), vol. 4210, pp. 312–322. Springer, Heidelberg (2006)
Krishnapuram, R., Keller, J.M.: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1, 98–110 (1993)
Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recognition 39, 2464–2477 (2006)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
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Mitra, S., Banka, H., Paik, J.H. (2007). Evolutionary Fuzzy Biclustering of Gene Expression Data. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_35
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DOI: https://doi.org/10.1007/978-3-540-72458-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72457-5
Online ISBN: 978-3-540-72458-2
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