A Census of Disease Ontologies

Author:

Haendel Melissa A.12,McMurry Julie A.1,Relevo Rose1,Mungall Christopher J.3,Robinson Peter N.4,Chute Christopher G.5

Affiliation:

1. Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon 97239, USA;

2. Linus Pauling Institute, Oregon State University, Corvallis, Oregon 97331, USA

3. Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

4. The Jackson Laboratory, Farmington, Connecticut 06032, USA

5. School of Medicine, School of Public Health, and School of Nursing, Johns Hopkins University, Baltimore, Maryland 21205, USA

Abstract

For centuries, humans have sought to classify diseases based on phenotypic presentation and available treatments. Today, a wide landscape of strategies, resources, and tools exist to classify patients and diseases. Ontologies can provide a robust foundation of logic for precise stratification and classification along diverse axes such as etiology, development, treatment, and genetics. Disease and phenotype ontologies are used in four primary ways: ( a) search, retrieval, and annotation of knowledge; ( b) data integration and analysis; ( c) clinical decision support; and ( d) knowledge discovery. Computational inference can connect existing knowledge and generate new insights and hypotheses about drug targets, prognosis prediction, or diagnosis. In this review, we examine the rise of disease and phenotype ontologies and the diverse ways they are represented and applied in biomedicine.

Publisher

Annual Reviews

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