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» Analysis of variance components in gene expression data
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BMCBI
2006
153views more  BMCBI 2006»
14 years 9 months ago
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating v...
Maureen A. Sartor, Craig R. Tomlinson, Scott C. We...
BMCBI
2011
14 years 4 months ago
Multivariate analysis of microarray data: differential expression and differential connection
Background: Typical analysis of microarray data ignores the correlation between gene expression values. In this paper we present a model for microarray data which specifically all...
Harri T. Kiiveri
BMCBI
2008
135views more  BMCBI 2008»
14 years 9 months ago
Knowledge-guided multi-scale independent component analysis for biomarker identification
Background: Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical...
Li Chen, Jianhua Xuan, Chen Wang, Ie-Ming Shih, Yu...
BMCBI
2010
135views more  BMCBI 2010»
14 years 9 months ago
Simple and flexible classification of gene expression microarrays via Swirls and Ripples
Background: A simple classification rule with few genes and parameters is desirable when applying a classification rule to new data. One popular simple classification rule, diagon...
Stuart G. Baker
BMCBI
2008
159views more  BMCBI 2008»
14 years 9 months ago
Multivariate hierarchical Bayesian model for differential gene expression analysis in microarray experiments
Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increas...
Hongya Zhao, Kwok-Leung Chan, Lee-Ming Cheng, Hong...