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COCOA
2009
Springer

Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting

13 years 10 months ago
Linear Coherent Bi-cluster Discovery via Line Detection and Sample Majority Voting
Discovering groups of genes that share common expression profiles is an important problem in DNA microarray analysis. Unfortunately, standard bi-clustering algorithms often fail to retrieve common expression groups because (1) genes only exhibit similar behaviors over a subset of conditions, and (2) genes may participate in more than one functional process and therefore belong to multiple groups. Many algorithms have been proposed to address these problems in the past decade; however, in addition to the above challenges most such algorithms are unable to discover linear coherent bi-clusters—a strict generalization of additive and multiplicative bi-clustering models. In this paper, we propose a novel bi-clustering algorithm that discovers linear coherent biclusters, based on first detecting linear correlations between pairs of gene expression profiles, then identifying groups by sample majority voting. Our experimental results on both synthetic and two real datasets, Saccharomyces ...
Yi Shi, Zhipeng Cai, Guohui Lin, Dale Schuurmans
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where COCOA
Authors Yi Shi, Zhipeng Cai, Guohui Lin, Dale Schuurmans
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