MRCluster: Mining Constant Row Bicluster in Gene Expression Data

Abstract:

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Biclustering is one of the important techniques for gene expression data analysis. A bicluster is a set of genes coherently expressed for a set of biological conditions. Various biclustering algorithms have been proposed to find biclusters of different types. However, most of them are not efficient. We propose a novel algorithm MRCluster to mine constant row biclusters from real-valued dataset. MRCluster uses Apriori property and several novel pruning techniques to mine biclusters efficiently. We compare our algorithm with a recent approach RAP, and experimental results show that MRCluster is much more efficient than RAP in mining biclusters with constant rows from real-valued gene expression data.

Info:

Periodical:

Edited by:

Robin G. Qiu and Yongfeng Ju

Pages:

628-633

DOI:

10.4028/www.scientific.net/AMM.135-136.628

Citation:

M. Miao et al., "MRCluster: Mining Constant Row Bicluster in Gene Expression Data", Applied Mechanics and Materials, Vols. 135-136, pp. 628-633, 2012

Online since:

October 2011

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Price:

$35.00

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