Evolutionary Fuzzy Biclustering of Gene Expression Data
- Cite this paper as:
- 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
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.
KeywordsMicroarray Genetic algorithms Possibilistic clustering Optimization
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