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|Title:||Consensus clustering and functional interpretation of gene expression data|
|Keywords:||Data clustering;Gene expression data|
|Citation:||Genome Biology, 5: R94, Nov 2004|
|Abstract:||Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene expression analysis. Here we introduce Consensus Clustering which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel Nuclear Factor-kB and Unfolded Protein Response regulated genes in certain B-cell lymphomas.|
|Appears in Collections:||Mathematical Science|
Dept of Computer Science Research Papers
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