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» Semantic Mining and Analysis of Gene Expression Data
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BMCBI
2010
196views more  BMCBI 2010»
14 years 7 months ago
Content-based microarray search using differential expression profiles
Background: With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experiment...
Jesse M. Engreitz, Alexander A. Morgan, Joel Dudle...
BMCBI
2006
126views more  BMCBI 2006»
14 years 9 months ago
OpWise: Operons aid the identification of differentially expressed genes in bacterial microarray experiments
Background: Differentially expressed genes are typically identified by analyzing the variation between replicate measurements. These procedures implicitly assume that there are no...
Morgan N. Price, Adam P. Arkin, Eric J. Alm
BMCBI
2005
148views more  BMCBI 2005»
14 years 9 months ago
Nonparametric tests for differential gene expression and interaction effects in multi-factorial microarray experiments
Background: Numerous nonparametric approaches have been proposed in literature to detect differential gene expression in the setting of two user-defined groups. However, there is ...
Xin Gao, Peter X. K. Song
BMCBI
2006
147views more  BMCBI 2006»
14 years 9 months ago
Grouping Gene Ontology terms to improve the assessment of gene set enrichment in microarray data
Background: Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find...
Alex Lewin, Ian C. Grieve
CIBCB
2006
IEEE
15 years 3 months ago
A Model-Free Greedy Gene Selection for Microarray Sample Class Prediction
— Microarray data analysis is notoriously challenging as it involves a huge number of genes compared to only a limited number of samples. Gene selection, to detect the most signi...
Yi Shi, Zhipeng Cai, Lizhe Xu, Wei Ren, Randy Goeb...