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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: McGill Centre for Bioinformatics, 3775 University Street, room 332, Montreal, Quebec, Canada, H3A 2B4. xchen@cs.washington.edu
authors.forename: Xiaoyu
authors.initials: X
authors.lastname: Chen
authors.forename: Mathieu
authors.initials: M
authors.lastname: Blanchette
mesh_terms: D001499:Bayes Theorem; D005544:Forecasting; D005786:Gene Expression Regulation / Q000235:genetics; D053263:Gene Regulatory Networks / Q000235:genetics; D005809:Genes, Regulator / Q000235:genetics; D006801:Humans; D012044:Regression Analysis; D014018:Tissue Distribution / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics
chemical_list: D014157:Transcription Factors