Paper Info
Title
Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees.
Abstract
BACKGROUND: In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression. RESULTS: We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure. CONCLUSION: Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.
Year
DOI
Venue
2007
10.1186/1471-2105-8-S10-S2
BMC Bioinformatics
Keywords
Field
DocType
gene expression,bayes theorem,algorithms,bioinformatics,bayesian network,cis regulatory module,gene expression regulation,forecasting,transcription factors,gene transcription,transcription factor binding site,microarrays,transcription factor,gene regulatory networks,regression analysis,regression tree
Transcriptional regulation,Gene,Biology,Regulation of gene expression,Bayesian network,Bioinformatics,Gene regulatory network,Genetics,Cis-regulatory module,DNA microarray,Bayes' theorem
Journal
Volume
Issue
ISSN
8
S-10
1471-2105
Citations 
PageRank 
References 
34
1.15
11
Authors
2
Name
Order
Citations
PageRank
Xiaoyu Chen1341.15
Mathieu Blanchette263162.65