title: Bayesian ontology querying for accurate and noise-tolerant semantic searches.
abstract: BACKGROUND
  Ontologies provide a structured representation of the concepts of a domain of knowledge as well as the relations between them. Attribute ontologies are used to describe the characteristics of the items of a domain, such as the functions of proteins or the signs and symptoms of disease, which opens the possibility of searching a database of items for the best match to a list of observed or desired attributes. However, naive search methods do not perform well on realistic data because of noise in the data, imprecision in typical queries and because individual items may not display all attributes of the category they belong to.
  
  
  RESULTS
  We present a method for combining ontological analysis with Bayesian networks to deal with noise, imprecision and attribute frequencies and demonstrate an application of our method as a differential diagnostic support system for human genetics.
  
  
  BACKGROUND
  We provide an implementation for the algorithm and the benchmark at http://compbio.charite.de/boqa/.
  
  
  BACKGROUND
  Sebastian.Bauer@charite.de or Peter.Robinson@charite.de
  
  
  BACKGROUND
  Supplementary Material for this article is available at Bioinformatics online.
authors: Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany. Sebastian.Bauer@charite.de
authors: Sebastian
authors: S
authors: Bauer
authors: Sebastian
authors: S
authors: Köhler
authors: Marcel H
authors: MH
authors: Schulz
authors: Peter N
authors: PN
authors: Robinson
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003661:Decision Support Techniques; D003937:Diagnosis, Differential; D006801:Humans; D015233:Models, Statistical; D012660:Semantics