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» Data-adaptive test statistics for microarray data
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131
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BMCBI
2010
181views more  BMCBI 2010»
15 years 2 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,...
146
Voted
BMCBI
2007
112views more  BMCBI 2007»
15 years 2 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...
115
Voted
BMCBI
2007
104views more  BMCBI 2007»
15 years 2 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...
123
Voted
BMCBI
2007
120views more  BMCBI 2007»
15 years 2 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
144
Voted
BMCBI
2004
124views more  BMCBI 2004»
15 years 2 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...