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dc.contributor.authorSmaili, Fatima Z.
dc.contributor.authorGao, Xin
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2018-11-12T11:50:06Z
dc.date.available2018-11-12T11:50:06Z
dc.date.issued2018-11-08
dc.identifier.citationSmaili FZ, Gao X, Hoehndorf R (2018) OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/bty933.
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/bty933
dc.identifier.urihttp://hdl.handle.net/10754/629859
dc.description.abstractMotivation:Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such. Results:We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology metadata. We apply a Word2Vec model that has been pre-trained on either a corpus or abstracts or full-text articles to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins in a similarity measure to predict protein-protein interaction on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations using mouse model phenotypes. We demonstrate that OPA2Vec significantly outperforms existing methods for predicting gene-disease associations. Using evidence from mouse models, we apply OPA2Vec to identify candidate genes for several thousand rare and orphan diseases. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability:https://github.com/bio-ontology-research-group/opa2vec.
dc.description.sponsorshipThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. FCC/1/1976-04, FCC/1/1976-06, URF/1/2602-01, URF/1/3007-01, URF/1/3412-01, URF/1/3450-01 and URF/1/3454-01.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty933/5165380
dc.rightsThis is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record is available online at: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty933/5165380.
dc.titleOPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.identifier.journalBioinformatics
dc.eprint.versionPost-print
dc.identifier.arxivid1804.10922
kaust.personSmaili, Fatima Zohra
kaust.personGao, Xin
kaust.personHoehndorf, Robert
kaust.grant.numberFCC/1/1976-04
kaust.grant.numberFCC/1/1976-06
kaust.grant.numberURF/1/2602-01
kaust.grant.numberURF/1/3007-01
kaust.grant.numberURF/1/3412-01
kaust.grant.numberURF/1/3450-01
kaust.grant.numberURF/1/3454-01
dc.versionv1
refterms.dateFOA2018-11-12T11:57:06Z
dc.date.published-online2018-11-08
dc.date.published-print2019-06-01
dc.date.posted2018-04-29


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