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dc.contributor.authorTaşan, Murat
dc.contributor.authorTian, Weidong
dc.contributor.authorHill, David
dc.contributor.authorGibbons, Francis D
dc.contributor.authorBlake, Judith A
dc.contributor.authorRoth, Frederick Phillip
dc.date.accessioned2010-10-28T20:13:54Z
dc.date.issued2008
dc.identifier.citationTaşan, Murat, Weidong Tian, David P. Hill, Francis D. Gibbons, Judith A. Blake, and Frederick P. Roth. 2008. An en masse phenotype and function prediction system for Mus musculus. Genome Biology 9:S8.en_US
dc.identifier.issn1465-6906en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4513841
dc.description.abstractBackground: 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. Conclusion: 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.en_US
dc.description.sponsorshipMolecular and Cellular Biologyen_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi:10.1186/gb-2008-9-s1-s8en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447542/pdf/en_US
dash.licenseLAA
dc.titleAn en masse Phenotype and Function Prediction System for Mus musculusen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalGenome Biologyen_US
dash.depositing.authorRoth, Frederick Phillip
dc.date.available2010-10-28T20:13:54Z
dc.identifier.doi10.1186/gb-2008-9-s1-s8*
dash.contributor.affiliatedRoth, Fritz


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