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» Clustering cancer gene expression data: a comparative study
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BMCBI
2006
126views more  BMCBI 2006»
15 years 6 months ago
OpWise: Operons aid the identification of differentially expressed genes in bacterial microarray experiments
Background: Differentially expressed genes are typically identified by analyzing the variation between replicate measurements. These procedures implicitly assume that there are no...
Morgan N. Price, Adam P. Arkin, Eric J. Alm
BMCBI
2010
120views more  BMCBI 2010»
15 years 6 months ago
Modeling expression quantitative trait loci in data combining ethnic populations
Background: Combining data from different ethnic populations in a study can increase efficacy of methods designed to identify expression quantitative trait loci (eQTL) compared to...
Ching-Lin Hsiao, Ie-Bin Lian, Ai-Ru Hsieh, Cathy S...
BMCBI
2010
164views more  BMCBI 2010»
15 years 3 months ago
Merged consensus clustering to assess and improve class discovery with microarray data
Background: One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a...
T. Ian Simpson, J. Douglas Armstrong, Andrew P. Ja...
ESANN
2008
15 years 7 months ago
A method for robust variable selection with significance assessment
Our goal is proposing an unbiased framework for gene expression analysis based on variable selection combined with a significance assessment step. We start by discussing the need ...
Annalisa Barla, Sofia Mosci, Lorenzo Rosasco, Ales...
BMCBI
2007
186views more  BMCBI 2007»
15 years 6 months ago
Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms
Background: Modeling cancer-related regulatory modules from gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as...
Jung-Hsien Chiang, Shih-Yi Chao