title: Causal analysis approaches in Ingenuity Pathway Analysis.
abstract: BACKGROUND
  Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets.
  
  
  RESULTS
  We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets.
  
  
  BACKGROUND
  The causal analytics tools 'Upstream Regulator Analysis', 'Mechanistic Networks', 'Causal Network Analysis' and 'Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com).
  
  
  BACKGROUND
  Supplementary material is available at Bioinformatics online.
authors: Ingenuity Systems, 1700 Seaport Boulevard, Redwood City, CA and Translational and Experimental Medicine-Bioinformatics, Sanofi-Aventis, 270 Albany Street, Cambridge, MA, USA.
authors: Andreas
authors: A
authors: Krämer
authors: Jeff
authors: J
authors: Green
authors: Jack
authors: J
authors: Pollard
authors: Stuart
authors: S
authors: Tugendreich
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics; D015984:Causality; D019295:Computational Biology; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D061307:Human Umbilical Vein Endothelial Cells / Q000378:metabolism; D006801:Humans; D051188:Knowledge Bases; D061986:MCF-7 Cells