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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 5 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 5 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 5 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 7 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 6 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...