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» Microarray Gene Expression Data Analysis
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BMCBI
2008
122views more  BMCBI 2008»
14 years 9 months ago
Determining gene expression on a single pair of microarrays
Background: In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently fe...
Robert W. Reid, Anthony A. Fodor
BMCBI
2006
131views more  BMCBI 2006»
14 years 9 months ago
SIMAGE: simulation of DNA-microarray gene expression data
Background: Simulation of DNA-microarray data serves at least three purposes: (i) optimizing the design of an intended DNA microarray experiment, (ii) comparing existing pre-proce...
Casper J. Albers, Ritsert C. Jansen, Jan Kok, Osca...
BMCBI
2007
163views more  BMCBI 2007»
14 years 9 months ago
Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA - a Bayesian approac
Background: Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge. Resul...
Carmen Pin, Mark Reuter
BMCBI
2007
112views more  BMCBI 2007»
14 years 9 months ago
Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis
Background: Gene clustering has been widely used to group genes with similar expression pattern in microarray data analysis. Subsequent enrichment analysis using predefined gene s...
Tae-Min Kim, Yeun-Jun Chung, Mun-Gan Rhyu, Myeong ...
BMCBI
2008
104views more  BMCBI 2008»
14 years 9 months ago
Missing value imputation improves clustering and interpretation of gene expression microarray data
Background: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value i...
Johannes Tuikkala, Laura Elo, Olli Nevalainen, Ter...