Mitra, Sushmita ; Banka, Haider ; Paik, Jiaul Hoque Evolutionary Fuzzy Biclustering of Gene Expression Data In: Rough Sets and Knowledge Technology, Springer, Berlin.
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Official URL: https://doi.org/10.1007/978-3-540-72458-2_35
Related URL: http://dx.doi.org/10.1007/978-3-540-72458-2_35
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. This is a preview of subscription content, log in via an institutio
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Source: | Copyright of this article belongs to Springer Berlin. |
| Keywords: | Microarray; Genetic algorithms; Possibilistic clustering Optimization. |
| ID Code: | 140173 |
| Deposited On: | 07 Sep 2025 06:07 |
| Last Modified: | 07 Sep 2025 06:07 |
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