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ABSTRACT: Motivation
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.Availability
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).Supplementary information
Supplementary material is available at Bioinformatics online.
SUBMITTER: Kramer A
PROVIDER: S-EPMC3928520 | biostudies-literature | 2014 Feb
REPOSITORIES: biostudies-literature
Krämer Andreas A Green Jeff J Pollard Jack J Tugendreich Stuart S
Bioinformatics (Oxford, England) 20131213 4
<h4>Motivation</h4>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.<h4>Results</h4>We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based o ...[more]