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    Integrating phenotype ontologies with PhenomeNET

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    Type
    Dataset
    Authors
    Rodriguez-Garcia, Miguel Angel cc
    Gkoutos, Georgios V.
    Schofield, Paul N.
    Hoehndorf, Robert cc
    KAUST Department
    Bio-Ontology Research Group (BORG)
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017
    Permanent link to this record
    http://hdl.handle.net/10754/663948
    
    Metadata
    Show full item record
    Abstract
    Abstract 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.
    Citation
    RodrĂ­Guez-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
    Publisher
    figshare
    DOI
    10.6084/m9.figshare.c.3958477.v1
    Relations
    Is Supplement To:
    • [Article]
      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: 10.1186/s13326-017-0167-4 HANDLE: 10754/626413
    ae974a485f413a2113503eed53cd6c53
    10.6084/m9.figshare.c.3958477.v1
    Scopus Count
    Collections
    Bio-Ontology Research Group (BORG); Bio-Ontology Research Group (BORG); Computer Science Program; Computational Bioscience Research Center (CBRC); Datasets; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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