Similarity-based search of model organism, disease and drug effect phenotypes

Handle URI:
http://hdl.handle.net/10754/346698
Title:
Similarity-based search of model organism, disease and drug effect phenotypes
Authors:
Hoehndorf, Robert ( 0000-0001-8149-5890 ) ; Gruenberger, Michael; Gkoutos, Georgios V; Schofield, Paul N
Abstract:
Background: Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity. Results: We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet. Conclusions: Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Similarity-based search of model organism, disease and drug effect phenotypes 2015, 6 (1) Journal of Biomedical Semantics
Publisher:
Springer Science + Business Media
Journal:
Journal of Biomedical Semantics
Issue Date:
19-Feb-2015
DOI:
10.1186/s13326-015-0001-9
Type:
Article
ISSN:
2041-1480
Additional Links:
http://www.jbiomedsem.com/content/6/1/6
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHoehndorf, Roberten
dc.contributor.authorGruenberger, Michaelen
dc.contributor.authorGkoutos, Georgios Ven
dc.contributor.authorSchofield, Paul Nen
dc.date.accessioned2015-03-16T05:26:14Zen
dc.date.available2015-03-16T05:26:14Zen
dc.date.issued2015-02-19en
dc.identifier.citationSimilarity-based search of model organism, disease and drug effect phenotypes 2015, 6 (1) Journal of Biomedical Semanticsen
dc.identifier.issn2041-1480en
dc.identifier.doi10.1186/s13326-015-0001-9en
dc.identifier.urihttp://hdl.handle.net/10754/346698en
dc.description.abstractBackground: Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity. Results: We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of phenotypes in real-time. It can be used to identify strains of model organisms that are phenotypically similar to human patients, diseases that are phenotypically similar to model organism phenotypes, or drug effect profiles that are similar to the phenotypes observed in a patient or model organism. PhenomeNET 2 is available at http://aber-owl.net/phenomenet. Conclusions: Phenotype-similarity searches can provide a powerful tool for the discovery and investigation of molecular mechanisms underlying an observed phenotypic manifestation. PhenomeNET 2 facilitates user-defined similarity searches and allows researchers to analyze their data within a large repository of human, mouse and rat phenotypes.en
dc.publisherSpringer Science + Business Mediaen
dc.relation.urlhttp://www.jbiomedsem.com/content/6/1/6en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.titleSimilarity-based search of model organism, disease and drug effect phenotypesen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Biomedical Semanticsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Computer Science, Aberystwyth Universityen
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridgeen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHoehndorf, Roberten
kaust.authorHoehndorf, Roberten
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