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dc.contributor.authorRodriguez-Garcia, Miguel Angel
dc.contributor.authorGkoutos, Georgios V.
dc.contributor.authorSchofield, Paul N.
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2020-06-30T13:47:03Z
dc.date.available2020-06-30T13:47:03Z
dc.date.issued2017
dc.identifier.citationRodrĂ­Guez-GarcĂ­A, M., Gkoutos, G., Schofield, P., & Hoehndorf, R. (2017). Integrating phenotype ontologies with PhenomeNET. Figshare. https://doi.org/10.6084/M9.FIGSHARE.C.3958477.V1
dc.identifier.doi10.6084/m9.figshare.c.3958477.v1
dc.identifier.urihttp://hdl.handle.net/10754/663948
dc.description.abstractAbstract Background Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Results Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies. Conclusions PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.
dc.publisherfigshare
dc.subjectBiochemistry
dc.subjectSpace Science
dc.subjectMedicine
dc.subjectGenetics
dc.subjectPharmacology
dc.subject69999 Biological Sciences not elsewhere classified
dc.subject80699 Information Systems not elsewhere classified
dc.subjectCancer
dc.subjectScience Policy
dc.subject110309 Infectious Diseases
dc.subjectComputational Biology
dc.titleIntegrating phenotype ontologies with PhenomeNET
dc.typeDataset
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.institutionCollege of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham
dc.contributor.institutionInstitute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust
dc.contributor.institutionInstitute of Biological, Environmental and Rural Sciences, Aberystwyth University
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridge
kaust.personRodriguez-Garcia, Miguel Angel
kaust.personRodriguez-Garcia, Miguel Angel
kaust.personHoehndorf, Robert
kaust.personHoehndorf, Robert
dc.relation.issupplementtoDOI:10.1186/s13326-017-0167-4
display.relations<b> Is Supplement To:</b><br/> <ul> <li><i>[Article]</i> <br/> Rodríguez-García MÁ, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Integrating phenotype ontologies with PhenomeNET. Journal of Biomedical Semantics 8. Available: http://dx.doi.org/10.1186/s13326-017-0167-4.. DOI: <a href="https://doi.org/10.1186/s13326-017-0167-4" >10.1186/s13326-017-0167-4</a> HANDLE: <a href="http://hdl.handle.net/10754/626413">10754/626413</a></li></ul>


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