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» Data-adaptive test statistics for microarray data
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BMCBI
2010
181views more  BMCBI 2010»
13 years 7 months ago
Intensity dependent estimation of noise in microarrays improves detection of differentially expressed genes
Background: In many microarray experiments, analysis is severely hindered by a major difficulty: the small number of samples for which expression data has been measured. When one ...
Amit Zeisel, Amnon Amir, Wolfgang J. Köstler,...
BMCBI
2007
112views more  BMCBI 2007»
13 years 7 months ago
Selecting dissimilar genes for multi-class classification, an application in cancer subtyping
Background: Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker ide...
Zhipeng Cai, Randy Goebel, Mohammad R. Salavatipou...
BMCBI
2007
104views more  BMCBI 2007»
13 years 7 months ago
Comparative evaluation of gene-set analysis methods
Background: Multiple data-analytic methods have been proposed for evaluating gene-expression levels in specific biological pathways, assessing differential expression associated w...
Qi Liu, Irina Dinu, Adeniyi J. Adewale, John D. Po...
BMCBI
2007
120views more  BMCBI 2007»
13 years 7 months ago
Re-sampling strategy to improve the estimation of number of null hypotheses in FDR control under strong correlation structures
Background: When conducting multiple hypothesis tests, it is important to control the number of false positives, or the False Discovery Rate (FDR). However, there is a tradeoff be...
Xin Lu, David L. Perkins
BMCBI
2004
124views more  BMCBI 2004»
13 years 7 months ago
Tests for finding complex patterns of differential expression in cancers: towards individualized medicine
Background: Microarray studies in cancer compare expression levels between two or more sample groups on thousands of genes. Data analysis follows a population-level approach (e.g....
James Lyons-Weiler, Satish Patel, Michael J. Becic...