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» Mixture Models for Detecting Differentially Expressed Genes ...
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BMCBI
2010
181views more  BMCBI 2010»
14 years 9 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»
14 years 9 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
81
Voted
GCB
2009
Springer
141views Biometrics» more  GCB 2009»
15 years 4 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»
14 years 9 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
79
Voted
FGCN
2008
IEEE
155views Communications» more  FGCN 2008»
14 years 11 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