Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases

Handle URI:
http://hdl.handle.net/10754/556899
Title:
Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases
Authors:
Hoehndorf, Robert ( 0000-0001-8149-5890 ) ; Schofield, Paul N.; Gkoutos, Georgios V.
Abstract:
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases 2015, 5:10888 Scientific Reports
Journal:
Scientific Reports
Issue Date:
8-Jun-2015
DOI:
10.1038/srep10888
Type:
Article
ISSN:
2045-2322
Additional Links:
http://www.nature.com/doifinder/10.1038/srep10888; http://arxiv.org/abs/1411.0450
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.authorSchofield, Paul N.en
dc.contributor.authorGkoutos, Georgios V.en
dc.date.accessioned2015-06-14T13:15:36Zen
dc.date.available2015-06-14T13:15:36Zen
dc.date.issued2015-06-08en
dc.identifier.citationAnalysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases 2015, 5:10888 Scientific Reportsen
dc.identifier.issn2045-2322en
dc.identifier.doi10.1038/srep10888en
dc.identifier.urihttp://hdl.handle.net/10754/556899en
dc.description.abstractPhenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.en
dc.relation.urlhttp://www.nature.com/doifinder/10.1038/srep10888en
dc.relation.urlhttp://arxiv.org/abs/1411.0450en
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/en
dc.subjectScientific dataen
dc.subjectSigns and symptomsen
dc.subjectExperimental models of diseaseen
dc.subjectDiseasesen
dc.titleAnalysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseasesen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalScientific Reportsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UKen
dc.contributor.institutionDepartment of Computer Science, Aberystwyth University, Llandinam Building, Aberystwyth, SY23 3DB, UKen
dc.identifier.arxividarXiv:1411.0450en
kaust.authorHoehndorf, Roberten
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