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» Mixture Models for Detecting Differentially Expressed Genes ...
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WCE
2007
13 years 10 months ago
Gene Selection for Tumor Classification Using Microarray Gene Expression Data
– In this paper we perform a t-test for significant gene expression analysis in different dimensions based on molecular profiles from microarray data, and compare several computa...
Krishna Yendrapalli, Ram B. Basnet, Srinivas Mukka...
BMCBI
2010
108views more  BMCBI 2010»
13 years 3 months ago
Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset
Background: Concomitant with the rise in the popularity of DNA microarrays has been a surge of proposed methods for the analysis of microarray data. Fully controlled "spike-i...
Qianqian Zhu, Jeffrey C. Miecznikowski, Marc S. Ha...
BMCBI
2008
166views more  BMCBI 2008»
13 years 8 months ago
Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual informa
Background: Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mecha...
Weijun Luo, Kurt D. Hankenson, Peter J. Woolf
BMCBI
2008
126views more  BMCBI 2008»
13 years 8 months ago
Combining Shapley value and statistics to the analysis of gene expression data in children exposed to air pollution
Background: In gene expression analysis, statistical tests for differential gene expression provide lists of candidate genes having, individually, a sufficiently low p-value. Howe...
Stefano Moretti, Danitsja van Leeuwen, Hans Gmuend...
BMCBI
2011
13 years 3 months ago
Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in i
Background: In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focuse...
Paolo G. V. Martini, Davide Risso, Gabriele Sales,...