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» Selecting maximally informative genes
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BMCBI
2002
188views more  BMCBI 2002»
13 years 7 months ago
The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data
Background: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic an...
David M. Mutch, Alvin Berger, Robert Mansourian, A...
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...
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
PSB
2004
13 years 9 months ago
Phylogenetic Motif Detection by Expectation-Maximization on Evolutionary Mixtures
ct The preferential conservation of transcription factor binding sites implies that non-coding sequence data from related species will prove a powerful asset to motif discovery. We...
Alan M. Moses, Derek Y. Chiang, Michael B. Eisen
BMCBI
2008
111views more  BMCBI 2008»
13 years 7 months ago
Comparative optimism in models involving both classical clinical and gene expression information
Background: In cancer research, most clinical variables have already been investigated and are now well established. The use of transcriptomic variables has raised two problems: r...
Caroline Truntzer, Delphine Maucort-Boulch, Pascal...