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BMCBI
2010
124views more  BMCBI 2010»
14 years 10 months ago
A factor model to analyze heterogeneity in gene expression
Background: Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene d...
Yuna Blum, Guillaume Le Mignon, Sandrine Lagarrigu...
BMCBI
2007
86views more  BMCBI 2007»
14 years 10 months ago
Toxicogenomic analysis incorporating operon-transcriptional coupling and toxicant concentration-expression response: analysis of
Background: Deficiencies in microarray technology cause unwanted variation in the hybridization signal, obscuring the true measurements of intracellular transcript levels. Here we...
William O. Ward, Carol D. Swartz, Steffen Porwolli...
BMCBI
2005
112views more  BMCBI 2005»
14 years 10 months ago
Vector analysis as a fast and easy method to compare gene expression responses between different experimental backgrounds
Background: Gene expression studies increasingly compare expression responses between different experimental backgrounds (genetic, physiological, or phylogenetic). By focusing on ...
Rainer Breitling, Patrick Armengaud, Anna Amtmann
BMCBI
2007
134views more  BMCBI 2007»
14 years 10 months ago
A framework for significance analysis of gene expression data using dimension reduction methods
Background: The most popular methods for significance analysis on microarray data are well suited to find genes differentially expressed across predefined categories. However, ide...
Lars Halvor Gidskehaug, Endre Anderssen, Arnar Fla...
BMCBI
2008
159views more  BMCBI 2008»
14 years 10 months ago
Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments
Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increas...
Hongya Zhao, Kwok-Leung Chan, Lee-Ming Cheng, Hong...