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BMCBI
2010
124views more  BMCBI 2010»
13 years 8 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
2005
132views more  BMCBI 2005»
13 years 8 months ago
Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein
Background: A large number of papers have been published on analysis of microarray data with particular emphasis on normalization of data, detection of differentially expressed ge...
Gajendra P. S. Raghava, Joon H. Han
BMCBI
2006
154views more  BMCBI 2006»
13 years 8 months ago
An improved procedure for gene selection from microarray experiments using false discovery rate criterion
Background: A large number of genes usually show differential expressions in a microarray experiment with two types of tissues, and the p-values of a proper statistical test are o...
James J. Yang, Mark C. K. Yang
BMCBI
2010
181views more  BMCBI 2010»
13 years 8 months ago
Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes
Background: In many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one ...
Amit Zeisel, Amnon Amir, Wolfgang J. Köstler,...
BMCBI
2008
126views more  BMCBI 2008»
13 years 8 months ago
Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution
Background: In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. Howe...
Stefano Moretti, Danitsja van Leeuwen, Hans Gmuend...