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dc.contributor.authorChen, Jun
dc.contributor.authorAlthagafi, Azza Th.
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2020-10-18T12:39:41Z
dc.date.available2020-04-06T13:00:32Z
dc.date.available2020-10-18T12:39:41Z
dc.date.issued2020-10-14
dc.date.submitted2020-03-30
dc.identifier.citationChen, J., Althagafi, A., & Hoehndorf, R. (2020). Predicting Candidate Genes From Phenotypes, Functions, And Anatomical Site Of Expression. Bioinformatics. doi:10.1093/bioinformatics/btaa879
dc.identifier.issn1367-4803
dc.identifier.pmid33051643
dc.identifier.doi10.1093/bioinformatics/btaa879
dc.identifier.urihttp://hdl.handle.net/10754/662446
dc.description.abstractMOTIVATION:Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models. RESULTS:We developed a novel graph-based machine learning method for biomedical ontologies which is able to exploit axioms in ontologies and other graph-structured data. Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression. We then develop a machine learning model to predict gene-disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state of the art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes which are associated with phenotypes, functions, or site of expression. AVAILABILITY:Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec.
dc.description.sponsorshipThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, URF/1/3790-01-01, FCC/1/1976-08-01, and FCC/1/1976-08-08.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa879/5922810
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titlePredicting Candidate Genes From Phenotypes, Functions, And Anatomical Site Of Expression.
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics (Oxford, England)
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionFaculty of Computing and Information Technology, Taif University, Taif, 26571, Saudi Arabia
kaust.personChen, Jun
kaust.personAlthagafi, Azza
kaust.personHoehndorf, Robert
kaust.grant.numberFCC/1/1976-08-01
kaust.grant.numberURF/1/3454-01-01
kaust.grant.numberURF/1/3790-01-01
dc.date.accepted2020-09-27
refterms.dateFOA2020-04-06T13:04:08Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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