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BMCBI
2006
122views more  BMCBI 2006»
13 years 7 months ago
A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets
Background: Gene selection is an important step when building predictors of disease state based on gene expression data. Gene selection generally improves performance and identifi...
Carmen Lai, Marcel J. T. Reinders, Laura J. van't ...
BMCBI
2010
100views more  BMCBI 2010»
13 years 7 months ago
A robust method for estimating gene expression states using Affymetrix microarray probe level data
Background: Microarray technology is a high-throughput method for measuring the expression levels of thousand of genes simultaneously. The observed intensities combine a non-speci...
Megu Ohtaki, Keiko Otani, Keiko Hiyama, Naomi Kame...
BMCBI
2004
75views more  BMCBI 2004»
13 years 7 months ago
Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes
Background: Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very f...
Hongying Jiang, Youping Deng, Huann-Sheng Chen, Li...
BMCBI
2008
160views more  BMCBI 2008»
13 years 7 months ago
A method for analyzing censored survival phenotype with gene expression data
Background: Survival time is an important clinical trait for many disease studies. Previous works have shown certain relationship between patients' gene expression profiles a...
Tongtong Wu, Wei Sun, Shinsheng Yuan, Chun-Houh Ch...
BMCBI
2005
110views more  BMCBI 2005»
13 years 7 months ago
Considerations when using the significance analysis of microarrays (SAM) algorithm
Background: Users of microarray technology typically strive to use universally acceptable data analysis strategies to determine significant expression changes in their experiments...
Ola Larsson, Claes Wahlestedt, James A. Timmons