title: An en masse phenotype and function prediction system for Mus musculus.
abstract: BACKGROUND
  Individual researchers are struggling to keep up with the accelerating emergence of high-throughput biological data, and to extract information that relates to their specific questions. Integration of accumulated evidence should permit researchers to form fewer - and more accurate - hypotheses for further study through experimentation.
  
  
  RESULTS
  Here a method previously used to predict Gene Ontology (GO) terms for Saccharomyces cerevisiae (Tian et al.: Combining guilt-by-association and guilt-by-profiling to predict Saccharomyces cerevisiae gene function. Genome Biol 2008, 9(Suppl 1):S7) is applied to predict GO terms and phenotypes for 21,603 Mus musculus genes, using a diverse collection of integrated data sources (including expression, interaction, and sequence-based data). This combined 'guilt-by-profiling' and 'guilt-by-association' approach optimizes the combination of two inference methodologies. Predictions at all levels of confidence are evaluated by examining genes not used in training, and top predictions are examined manually using available literature and knowledge base resources.
  
  
  CONCLUSIONS
  We assigned a confidence score to each gene/term combination. The results provided high prediction performance, with nearly every GO term achieving greater than 40% precision at 1% recall. Among the 36 novel predictions for GO terms and 40 for phenotypes that were studied manually, >80% and >40%, respectively, were identified as accurate. We also illustrate that a combination of 'guilt-by-profiling' and 'guilt-by-association' outperforms either approach alone in their application to M. musculus.
authors.affiliation: Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Longwood Avenue, Boston, Massachusetts 02115, USA.
authors.forename: Murat
authors.initials: M
authors.lastname: Taşan
authors.forename: Weidong
authors.initials: W
authors.lastname: Tian
authors.forename: David P
authors.initials: DP
authors.lastname: Hill
authors.forename: Francis D
authors.initials: FD
authors.lastname: Gibbons
authors.forename: Judith A
authors.initials: JA
authors.lastname: Blake
authors.forename: Frederick P
authors.initials: FP
authors.lastname: Roth
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D053858:Metabolic Networks and Pathways; D051379:Mice / Q000235:genetics*; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D009626:Terminology as Topic
chemical_list: D011506:Proteins