title: biRte: Bayesian inference of context-specific regulator activities and transcriptional networks.
abstract: In the last years there has been an increasing effort to computationally model and predict the influence of regulators (transcription factors, miRNAs) on gene expression. Here we introduce biRte as a computationally attractive approach combining Bayesian inference of regulator activities with network reverse engineering. biRte integrates target gene predictions with different omics data entities (e.g. miRNA and mRNA data) into a joint probabilistic framework. The utility of our method is tested in extensive simulation studies and demonstrated with applications from prostate cancer and Escherichia coli growth control. The resulting regulatory networks generally show a good agreement with the biological literature.
  
  
  METHODS
  biRte is available on Bioconductor (http://bioconductor.org).
  
  
  BACKGROUND
  frohlich@bit.uni-bonn.de
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: University of Bonn, Institute for Computer Science, Römerstr. 164, 53117 Bonn, Germany.
authors.forename: Holger
authors.initials: H
authors.lastname: Fröhlich
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics; D015870:Gene Expression; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000235:genetics; D012333:RNA, Messenger / Q000378:metabolism; D012984:Software; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger; D014157:Transcription Factors