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» Classification of microarray data using gene networks
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BMCBI
2006
100views more  BMCBI 2006»
15 years 4 months ago
Empirical array quality weights in the analysis of microarray data
Background: Assessment of array quality is an essential step in the analysis of data from microarray experiments. Once detected, less reliable arrays are typically excluded or &qu...
Matthew E. Ritchie, Dileepa S. Diyagama, Jody Neil...
BMCBI
2007
135views more  BMCBI 2007»
15 years 4 months ago
Detecting multivariate differentially expressed genes
Background: Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariat...
Roland Nilsson, José M. Peña, Johan ...
BMCBI
2008
137views more  BMCBI 2008»
15 years 4 months ago
VennMaster: Area-proportional Euler diagrams for functional GO analysis of microarrays
Background: Microarray experiments generate vast amounts of data. The functional context of differentially expressed genes can be assessed by querying the Gene Ontology (GO) datab...
Hans A. Kestler, André Müller, Johann ...
BMCBI
2004
146views more  BMCBI 2004»
15 years 4 months ago
Multivariate search for differentially expressed gene combinations
Background: To identify differentially expressed genes, it is standard practice to test a twosample hypothesis for each gene with a proper adjustment for multiple testing. Such te...
Yuanhui Xiao, Robert D. Frisina, Alexander Gordon,...
JBI
2004
171views Bioinformatics» more  JBI 2004»
15 years 5 months ago
Consensus Clustering and Functional Interpretation of Gene Expression Data
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus s...
Paul Kellam, Stephen Swift, Allan Tucker, Veronica...