MRCluster: Mining Constant Row Bicluster in Gene Expression Data

Miao Miao; Xue Qun Shang; Jia Cai Liu; Miao Wang

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

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 135-136MRCluster: Mining Constant Row Bicluster in Gene...

MRCluster: Mining Constant Row Bicluster in Gene Expression Data

Abstract:

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.

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Periodical:

Applied Mechanics and Materials (Volumes 135-136)

Pages:

628-633

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.135-136.628

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Online since:

October 2011

Authors:

Miao Miao, Xue Qun Shang, Jia Cai Liu, Miao Wang

Keywords:

Biclustering, Constant Row Bicluster, Micro-Array, Rangesupport, Real-Valued Data

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