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dc.creatorKim, Wan Kyuen
dc.creatorKrumpelman, Chaseen
dc.creatorMarcotte, Edward M.en
dc.date.accessioned2014-12-15T17:10:16Zen
dc.date.available2014-12-15T17:10:16Zen
dc.date.issued2008-06-27en
dc.identifier.citationKim, Wan K., Chase Krumpelman, and Edward M. Marcotte. “Inferring Mouse Gene Functions from Genomic-Scale Data Using a Combined Functional Network/classification Strategy.” Genome Biology 9, no. Suppl 1 (June 27, 2008): S5. doi:10.1186/gb-2008-9-s1-s5.en
dc.identifier.urihttp://hdl.handle.net/2152/27855en
dc.descriptionCenter for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, Speedway, Austin, Texas 78712, USAen
dc.description.abstractThe complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, but the majority of genes as yet still of unknown function. Within the context of the MouseFunc competition, we developed and applied two distinct large-scale data mining approaches to infer the functions (Gene Ontology annotations) of mouse genes from experimental observations from available functional genomics, proteomics, comparative genomics, and phenotypic data. The two strategies — the first using classifiers to map features to annotations, the second propagating annotations from characterized genes to uncharacterized genes along edges in a network constructed from the features — offer alternative and possibly complementary approaches to providing functional annotations. Here, we re-implement and evaluate these approaches and their combination for their ability to predict the proper functional annotations of genes in the MouseFunc data set. We show that, when controlling for the same set of input features, the network approach generally outperformed a naïve Bayesian classifier approach, while their combination offers some improvement over either independently. We make our observations of predictive performance on the MouseFunc competition hold-out set, as well as on a ten-fold cross-validation of the MouseFunc data. Across all 1,339 annotated genes in the MouseFunc test set, the median predictive power was quite strong (median area under a receiver operating characteristic plot of 0.865 and average precision of 0.195), indicating that a mining-based strategy with existing data is a promising path towards discovering mammalian gene functions. As one product of this work, a high-confidence subset of the functional mouse gene network was produced — spanning >70% of mouse genes with >1.6 million associations — that is predictive of mouse (and therefore often human) gene function and functional associations. The network should be generally useful for mammalian gene functional analyses, such as for predicting interactions, inferring functional connections between genes and pathways, and prioritizing candidate genes. The network and all predictions are available on the worldwide web.en
dc.description.sponsorshipen
dc.language.isoEnglishen
dc.publisherGenome Biologyen
dc.rightsAdministrative deposit of works to UT Digital Repository: This works author(s) is or was a University faculty member, student or staff member; this article is already available through open access at http://www.biomedcentral.com. The public license is specified as CC-BY: http://creativecommons.org/licenses/by/4.0/. The library makes the deposit as a matter of fair use (for scholarly, educational, and research purposes), and to preserve the work and further secure public access to the works of the University.en
dc.subjectmouse gene functionsen
dc.subjectgenomic-scale dataen
dc.subjectnetwork/classification strategyen
dc.titleInferring mouse gene functions from genomic-scale data using a combined functional network/classification strategyen
dc.typeArticleen
dc.description.departmentCenter for Systems and Synthetic Biologyen
dc.description.departmentInstitute for Cellular and Molecular Biologyen
dc.description.catalogingnotemarcotte@icmb.utexas.eduen
dc.identifier.Filenamegb-2008-9-s1-s5en
dc.identifier.doidoi:10.1186/gb-2008-9-s1-s5en


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