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BMCBI
2010
181views more  BMCBI 2010»
13 years 7 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
138views more  BMCBI 2008»
13 years 7 months ago
M-BISON: Microarray-based integration of data sources using networks
Background: The accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variabi...
Bernie J. Daigle Jr., Russ B. Altman
GCB
2009
Springer
141views Biometrics» more  GCB 2009»
14 years 2 months ago
Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series
: A wealth of time series of microarray measurements have become available over recent years. Several two-sample tests for detecting differential gene expression in these time seri...
Oliver Stegle, Katherine J. Denby, David L. Wild, ...
BMCBI
2005
148views more  BMCBI 2005»
13 years 7 months ago
Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments
Background: Numerous nonparametric approaches have been proposed in literature to detect differential gene expression in the setting of two user-defined groups. However, there is ...
Xin Gao, Peter X. K. Song
FGCN
2008
IEEE
155views Communications» more  FGCN 2008»
13 years 9 months ago
Modeling the Marginal Distribution of Gene Expression with Mixture Models
We report the results of fitting mixture models to the distribution of expression values for individual genes over a broad range of normal tissues, which we call the marginal expr...
Edward Wijaya, Hajime Harada, Paul Horton