A CLOSE-TO OPTIMUM BI-CLUSTERING ALGORITHM FOR MICROARRAY GENE EXPRESSION DATA

Motivation: Bi-clustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. The bi-clustering strategy has been widely used for analyses of gene expression data and beyond since it was first proposed in 2000 since it provides much increased flexibility and analysis power in identifying co-expressed genes under some but not necessarily all conditions, compared to traditional clustering methods. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the bi-clustering problem.

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