title: Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.
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.
authors.affiliation: 1] Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia [2] Computer, Electrical and Mathematical Sciences &Engineering Division, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.
authors.forename: Robert
authors.initials: R
authors.lastname: Hoehndorf
authors.affiliation: Department of Physiology, Development &Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK.
authors.forename: Paul N
authors.initials: PN
authors.lastname: Schofield
authors.affiliation: Department of Computer Science, Aberystwyth University, Llandinam Building, Aberystwyth, SY23 3DB, UK.
authors.forename: Georgios V
authors.initials: GV
authors.lastname: Gkoutos
mesh_terms: D000818:Animals; D064229:Biological Ontologies; D057225:Data Mining; D016208:Databases, Factual; D030342:Genetic Diseases, Inborn; D006801:Humans; D007239:Infections; D051379:Mice