title: Bayesian integration of networks without gold standards.
abstract: BACKGROUND
  Biological experiments give insight into networks of processes inside a cell, but are subject to error and uncertainty. However, due to the overlap between the large number of experiments reported in public databases it is possible to assess the chances of individual observations being correct. In order to do so, existing methods rely on high-quality 'gold standard' reference networks, but such reference networks are not always available.
  
  
  RESULTS
  We present a novel algorithm for computing the probability of network interactions that operates without gold standard reference data. We show that our algorithm outperforms existing gold standard-based methods. Finally, we apply the new algorithm to a large collection of genetic interaction and protein-protein interaction experiments.
  
  
  BACKGROUND
  The integrated dataset and a reference implementation of the algorithm as a plug-in for the Ondex data integration framework are available for download at http://bio-nexus.ncl.ac.uk/projects/nogold/
authors.affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Jochen
authors.initials: J
authors.lastname: Weile
authors.forename: Katherine
authors.initials: K
authors.lastname: James
authors.forename: Jennifer
authors.initials: J
authors.lastname: Hallinan
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Cockell
authors.forename: Phillip
authors.initials: P
authors.lastname: Lord
authors.forename: Anil
authors.initials: A
authors.lastname: Wipat
authors.forename: Darren J
authors.initials: DJ
authors.lastname: Wilkinson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004843:Epistasis, Genetic; D016013:Likelihood Functions; D025941:Protein Interaction Mapping / Q000379:methods / Q000592:standards*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism