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BMCBI
2005
98views more  BMCBI 2005»
13 years 4 months ago
The effects of normalization on the correlation structure of microarray data
Background: Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test-...
Xing Qiu, Andrew I. Brooks, Lev Klebanov, Andrei Y...
BMCBI
2005
126views more  BMCBI 2005»
13 years 4 months ago
Integrative analysis of multiple gene expression profiles with quality-adjusted effect size models
Background: With the explosion of microarray studies, an enormous amount of data is being produced. Systematic integration of gene expression data from different sources increases...
Pingzhao Hu, Celia M. T. Greenwood, Joseph Beyene
BMCBI
2005
152views more  BMCBI 2005»
13 years 4 months ago
CoPub Mapper: mining MEDLINE based on search term co-publication
Background: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to ...
Blaise T. F. Alako, Antoine Veldhoven, Sjozef van ...
BIOINFORMATICS
2005
72views more  BIOINFORMATICS 2005»
13 years 4 months ago
Use of within-array replicate spots for assessing differential expression in microarray experiments
Motivation. Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the...
Gordon K. Smyth, Joëlle Michaud, Hamish S. Sc...
BMCBI
2006
103views more  BMCBI 2006»
13 years 4 months ago
Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression
Background: The identification of differentially expressed genes (DEGs) from Affymetrix GeneChips arrays is currently done by first computing expression levels from the low-level ...
Sébastien Lemieux
BMCBI
2006
154views more  BMCBI 2006»
13 years 4 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
2006
165views more  BMCBI 2006»
13 years 4 months ago
A stable gene selection in microarray data analysis
Background: Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most signi...
Kun Yang, Zhipeng Cai, Jianzhong Li, Guohui Lin
BMCBI
2006
126views more  BMCBI 2006»
13 years 4 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
2008
122views more  BMCBI 2008»
13 years 4 months ago
Determining gene expression on a single pair of microarrays
Background: In microarray experiments the numbers of replicates are often limited due to factors such as cost, availability of sample or poor hybridization. There are currently fe...
Robert W. Reid, Anthony A. Fodor
BMCBI
2008
107views more  BMCBI 2008»
13 years 4 months ago
A mixture model approach to sample size estimation in two-sample comparative microarray experiments
Background: Choosing the appropriate sample size is an important step in the design of a microarray experiment, and recently methods have been proposed that estimate sample sizes ...
Tommy S. Jørstad, Herman Midelfart, Atle M....