DermO; an ontology for the description of dermatologic disease. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Fisher, Hannah; Hoehndorf, Robert; Bazelato, Bruno; Dadras, Soheil; King, Lloyd; Gkoutos, Georgios; Sundberg, John; Schofield, Paul
• Is Part Of: Journal of biomedical semantics. Volume 7:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-12
• Subjects: Dermatology -- Ontology -- Disease -- Dermatopathology; Semantics -- Periodicals; Medicine -- Research -- Periodicals; Biology -- Research -- Periodicals; Computer systems -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1186/s13326-016-0085-x
• Languages: English
• ISSNs: 2041-1480

Abstract

Abstract Background There have been repeated initiatives to produce standard nosologies and terminologies for cutaneous disease, some dedicated to the domain and some part of bigger terminologies such as ICD-10. Recently, formally structured terminologies, ontologies, have been widely developed in many areas of biomedical research. Primarily, these address the aim of providing comprehensive working terminologies for domains of knowledge, but because of the knowledge contained in the relationships between terms they can also be used computationally for many purposes. Results We have developed an ontology of cutaneous disease, constructed manually by domain experts. With more than 3000 terms, DermO represents the most comprehensive formal dermatological disease terminology available. The disease entities are categorized in 20 upper level terms, which use a variety of features such as anatomical location, heritability, affected cell or tissue type, or etiology, as the features for classification, in line with professional practice and nosology in dermatology. Available in OBO flatfile and OWL 2 formats, it is integrated semantically with other ontologies and terminologies describing diseases and phenotypes. We demonstrate the application of DermO to text mining the biomedical literature and in the creation of a network describing the phenotypic relationships between cutaneous diseases. Conclusions DermO is an ontology with broad coverage of the domain of dermatologic disease and we demonstrate here its utility for text mining and investigation of phenotypic relationships between dermatologic disorders. We envision that in the future it may be applied to the creation and mining of electronic health records, clinical training and basic research, as it supports automated inference and reasoning, and for the broader integration of skin disease information with that from other domains.