title: Reverse engineering of genetic networks with Bayesian networks.
abstract: This paper provides a brief introduction to learning Bayesian networks from gene-expression data. The method is contrasted with other approaches to the reverse engineering of biochemical networks, and the Bayesian learning paradigm is briefly described. The article demonstrates an application to a simple synthetic toy problem and evaluates the inference performance in terms of ROC (receiver operator characteristic) curves.
authors.affiliation: Biomathematics and Statistics Scotland (BioSS), JCMB, The King's Buildings, Edinburgh EH9 3JZ, Scotland, U.K. dirk@bioss.sari.ac.uk
authors.forename: D
authors.initials: D
authors.lastname: Husmeier
mesh_terms: D001499:Bayes Theorem; D015870:Gene Expression; D012372:ROC Curve; D015203:Reproducibility of Results