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JCB
2000
103views more  JCB 2000»
13 years 5 months ago
Testing for Differentially-Expressed Genes by Maximum-Likelihood Analysis of Microarray Data
Although two-color uorescent DNA microarrays are now standard equipment in many molecular biology laboratories, methods for identifying differentially expressed genes in microarra...
Trey Ideker, Vesteinn Thorsson, Andrew F. Siegel, ...
BMCBI
2005
107views more  BMCBI 2005»
13 years 5 months ago
Identifying differential expression in multiple SAGE libraries: an overdispersed log-linear model approach
Background: In testing for differential gene expression involving multiple serial analysis of gene expression (SAGE) libraries, it is critical to account for both between and with...
Jun Lu, John K. Tomfohr, Thomas B. Kepler
BMCBI
2011
12 years 9 months ago
Gene set analysis for longitudinal gene expression data
Background: Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. ...
Ke Zhang, Haiyan Wang, Arne C. Bathke, Solomon W. ...
BMCBI
2010
124views more  BMCBI 2010»
13 years 5 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
2004
158views more  BMCBI 2004»
13 years 5 months ago
A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data
Background: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two...
Kayvan Najarian, Maryam Zaheri, Ali Ajdari Rad, Si...