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BMCBI
2008
98views more  BMCBI 2008»
13 years 7 months ago
Empirical Bayes models for multiple probe type microarrays at the probe level
Background: When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are...
Magnus Åstrand, Petter Mostad, Mats Rudemo
BMCBI
2010
96views more  BMCBI 2010»
13 years 7 months ago
A statistical framework for differential network analysis from microarray data
Background: It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of gene...
Ryan Gill, Somnath Datta, Susmita Datta
BMCBI
2006
239views more  BMCBI 2006»
13 years 7 months ago
Applying dynamic Bayesian networks to perturbed gene expression data
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics,...
Norbert Dojer, Anna Gambin, Andrzej Mizera, Bartek...
BIOCOMP
2006
13 years 9 months ago
Computational Inference of Compound-induced Anti-inflammatory Effects Across Time in an Adjuvant-induced Arthritis Rat Model
A number of diseases, such as arthritis and cardiovascular disorders impacting the lives of many people have strong inflammatory components. To elucidate the antiinflammatory mecha...
Jing Yu, Gabriel Helmlinger, Muriel Saulnier, Anna...
BMCBI
2005
98views more  BMCBI 2005»
13 years 7 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...