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» Improving gene set analysis of microarray data by SAM-GS
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BMCBI
2008
138views more  BMCBI 2008»
13 years 7 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
BMCBI
2010
96views more  BMCBI 2010»
13 years 7 months ago
A statistical framework for differential network analysis from microarray data
Background: It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of gene...
Ryan Gill, Somnath Datta, Susmita Datta
BMCBI
2005
169views more  BMCBI 2005»
13 years 7 months ago
An adaptive method for cDNA microarray normalization
Background: Normalization is a critical step in analysis of gene expression profiles. For duallabeled arrays, global normalization assumes that the majority of the genes on the ar...
Yingdong Zhao, Ming-Chung Li, Richard Simon
BMCBI
2008
104views more  BMCBI 2008»
13 years 7 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...
BMCBI
2007
173views more  BMCBI 2007»
13 years 7 months ago
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that a...
Malik Yousef, Segun Jung, Louise C. Showe, Michael...