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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
2006
154views more  BMCBI 2006»
13 years 7 months ago
Analysis with respect to instrumental variables for the exploration of microarray data structures
Background: Evaluating the importance of the different sources of variations is essential in microarray data experiments. Complex experimental designs generally include various fa...
Florent Baty, Michaël Facompré, Jan Wi...
BMCBI
2010
153views more  BMCBI 2010»
13 years 6 months ago
DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules
Background: Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differen...
Bruno M. Tesson, Rainer Breitling, Ritsert C. Jans...
BMCBI
2008
148views more  BMCBI 2008»
13 years 7 months ago
BATS: a Bayesian user-friendly software for Analyzing Time Series microarray experiments
Summary: BATS is a user-friendly software for Bayesian Analysis of Time Series microarray experiments based on the novel, truly functional and fully Bayesian approach proposed in ...
Claudia Angelini, Luisa Cutillo, Daniela De Candit...
SAC
2008
ACM
13 years 6 months ago
Strangeness-based feature weighting and classification of gene expression profiles
Achieving high classification accuracy is a major challenge in the diagnosis of cancer types based on gene expression profiles. These profiles are notoriously noisy in that a larg...
Haifeng Shao, Bei Yu, Joseph H. Nadeau