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» Modeling Microarray Data: Interpreting and communicating the...
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BMCBI
2007
171views more  BMCBI 2007»
14 years 11 months ago
Classification of microarray data using gene networks
Background: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) a...
Franck Rapaport, Andrei Zinovyev, Marie Dutreix, E...
BMCBI
2007
134views more  BMCBI 2007»
14 years 12 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
2006
130views more  BMCBI 2006»
14 years 11 months ago
CARMA: A platform for analyzing microarray datasets that incorporate replicate measures
Background: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust ex...
Kevin A. Greer, Matthew R. McReynolds, Heddwen L. ...
BMCBI
2006
147views more  BMCBI 2006»
14 years 11 months ago
Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data
Background: Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find...
Alex Lewin, Ian C. Grieve
BMCBI
2004
169views more  BMCBI 2004»
14 years 11 months ago
A power law global error model for the identification of differentially expressed genes in microarray data
Background: High-density oligonucleotide microarray technology enables the discovery of genes that are transcriptionally modulated in different biological samples due to physiolog...
Norman Pavelka, Mattia Pelizzola, Caterina Vizzard...