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GECCO
2004
Springer
104views Optimization» more  GECCO 2004»
14 years 27 days ago
A Genetic Approach for Gene Selection on Microarray Expression Data
Abstract. Microarrays allow simultaneous measurement of the expression levels of thousands of genes in cells under different physiological or disease states. Because the number of...
Yong-Hyuk Kim, Su-Yeon Lee, Byung Ro Moon
BMCBI
2005
124views more  BMCBI 2005»
13 years 7 months ago
ErmineJ: Tool for functional analysis of gene expression data sets
Background: It is common for the results of a microarray study to be analyzed in the context of biologically-motivated groups of genes such as pathways or Gene Ontology categories...
Homin K. Lee, William Braynen, Kiran Keshav, Paul ...
BMCBI
2007
182views more  BMCBI 2007»
13 years 7 months ago
Additive risk survival model with microarray data
Background: Microarray techniques survey gene expressions on a global scale. Extensive biomedical studies have been designed to discover subsets of genes that are associated with ...
Shuangge Ma, Jian Huang
BMCBI
2008
190views more  BMCBI 2008»
13 years 7 months ago
Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes
Background: Gene expression data frequently contain missing values, however, most downstream analyses for microarray experiments require complete data. In the literature many meth...
Guy N. Brock, John R. Shaffer, Richard E. Blakesle...
ISMB
2001
13 years 9 months ago
Molecular classification of multiple tumor types
Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished...
Chen-Hsiang Yeang, Sridhar Ramaswamy, Pablo Tamayo...