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 8,000 diseases. We demonstrate that our method generates
phenotypes that 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 share signs and symptoms cluster
together, and we use this network to identify phenotypic disease modules
