Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases
KAUST DepartmentBio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2014-11-03
Online Publication Date2015-06-08
Print Publication Date2015-09
Permanent link to this recordhttp://hdl.handle.net/10754/556899
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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.
CitationAnalysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases 2015, 5:10888 Scientific Reports
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- Authors: Masino AJ, Dechene ET, Dulik MC, Wilkens A, Spinner NB, Krantz ID, Pennington JW, Robinson PN, White PS
- Issue date: 2014 Jul 21
- The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data.
- Authors: Köhler S, Doelken SC, Mungall CJ, Bauer S, Firth HV, Bailleul-Forestier I, Black GC, Brown DL, Brudno M, Campbell J, FitzPatrick DR, Eppig JT, Jackson AP, Freson K, Girdea M, Helbig I, Hurst JA, Jähn J, Jackson LG, Kelly AM, Ledbetter DH, Mansour S, Martin CL, Moss C, Mumford A, Ouwehand WH, Park SM, Riggs ER, Scott RH, Sisodiya S, Van Vooren S, Wapner RJ, Wilkie AO, Wright CF, Vulto-van Silfhout AT, de Leeuw N, de Vries BB, Washingthon NL, Smith CL, Westerfield M, Schofield P, Ruef BJ, Gkoutos GV, Haendel M, Smedley D, Lewis SE, Robinson PN
- Issue date: 2014 Jan
- Linking human diseases to animal models using ontology-based phenotype annotation.
- Authors: Washington NL, Haendel MA, Mungall CJ, Ashburner M, Westerfield M, Lewis SE
- Issue date: 2009 Nov
- Biomarker identification using text mining.
- Authors: Li H, Liu C
- Issue date: 2012