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» Step-Down FDR Procedures for Large Numbers of Hypotheses
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BMCBI
2006
154views more  BMCBI 2006»
13 years 9 months ago
An improved procedure for gene selection from microarray experiments using false discovery rate criterion
Background: A large number of genes usually show differential expressions in a microarray experiment with two types of tissues, and the p-values of a proper statistical test are o...
James J. Yang, Mark C. K. Yang
BMCBI
2005
163views more  BMCBI 2005»
13 years 9 months ago
Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data
Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustm...
Nitin Jain, HyungJun Cho, Michael O'Connell, Jae K...
BMCBI
2008
128views more  BMCBI 2008»
13 years 10 months ago
Nonparametric relevance-shifted multiple testing procedures for the analysis of high-dimensional multivariate data with small sa
Background: In many research areas it is necessary to find differences between treatment groups with several variables. For example, studies of microarray data seek to find a sign...
Cornelia Frömke, Ludwig A. Hothorn, Siegfried...
RECOMB
2010
Springer
13 years 11 months ago
Algorithms for Detecting Significantly Mutated Pathways in Cancer
Abstract. Recent genome sequencing studies have shown that the somatic mutations that drive cancer development are distributed across a large number of genes. This mutational heter...
Fabio Vandin, Eli Upfal, Benjamin J. Raphael
HIS
2004
13 years 11 months ago
Adaptive Boosting with Leader based Learners for Classification of Large Handwritten Data
Boosting is a general method for improving the accuracy of a learning algorithm. AdaBoost, short form for Adaptive Boosting method, consists of repeated use of a weak or a base le...
T. Ravindra Babu, M. Narasimha Murty, Vijay K. Agr...