Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases

TL;DR

This study uses semantic text-mining to analyze phenotypes across thousands of diseases, creating a network that reveals phenotype modules and links to genetic causes, aiding understanding of complex and infectious diseases.

Contribution

It introduces a novel semantic text-mining approach to identify disease phenotypes and construct a human disease network based on shared signs and symptoms.

Findings

Accurately identifies disease-associated genes in humans and mice.

Creates a disease network clustering diseases by phenotype similarity.

Reveals phenotype modules across diverse disease categories.

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 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.