Disease Ontology: a backbone for disease semantic integration

By L. Schriml, Cesar Arze, Suvarna Nadendla, Yu Chang, Mark Mazaitis, V. Felix, Gang Feng, Warren A. Kibbe. Nucleic Acids Res., 2011

961 citations recorded by the source corpus.

The Disease Ontology (DO) database (http://disease-ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM disease-specific terms and identifiers. The DO is utilized for disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human disease in biomedical ontologies (e.g. IDO, Cell line ontology, NIFSTD ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://diseaseontology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and disease biomedical data through the lens of human disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bf269c5678a5151c5785f1e121e5bf3021f7ad90

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Citations in the corpus

NCBI disease corpus: A resource for disease name recognition and concept normalization (2014, background): “Other possible terminology resources that could be considered for such purposes are the Disease Ontology [ 43 ] and the Human Phenotype Ontology [ 44 ] .”
SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis (2013, positive): “Tosimplifytheuserinterface,datasetsweregroupedby relatedorganortissueusingdiseaseontologies[10].”
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations (2013, methodology): “We evaluated several domain ontologies for coverage of the GWAS data, including Medical Subject Headings (MeSH), the Human Phenotype Ontology (12), the Disease Ontology (13) and the Experimental Factor Ontology (EFO) (14).”
The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data (2013, background): “As such, 39% (3956) of the HPO terms contain cross-references, with 98% of the references pointing to Uniﬁed Medical Language System and Medical Subject Headings, references that are especially helpful for linking to resources such as the Disease Ontology (8).”
Systematic integration of biomedical knowledge prioritizes drugs for repurposing (2017, methodology): “For example, the Disease Ontology includes mappings to MeSH, UMLS, and the ICD, several of which we submitted during the course of this study ( Himmelstein, 2015e ).”
The BioGRID interaction database: 2015 update (2014, background): “The IMS has been elaborated to include more than a dozen comprehensive new ontologies (77–79) that allow curators to unambiguously record new details of any relationship, such as cell lines, phenotypes, small molecules, alleles, diseases, tissues and enzymes (Figure 3).”
Using clusterProfiler to characterize multiomics data (2024, neutral): “Popular databases for functional enrichment analysis are GO 85 , KEGG 50 , disease ontology (DO) 86 , DisGeNET 87 , Network of Cancer Genes (NCG) 88 , MSigDb 13 , WikiPathways 14 and Reactome pathway 89 , among others.”
DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes (2015, methodology): “In addition, we also provide annotations to the Human Disease Ontology (HDO) (26) S1 shows the coverage of DisGeNET diseases with the different disease terminologies and ontologies.”
Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data (2014, background): “Since the previous DO NAR report (7), the subtypes of each body system disease have been reviewed and classified.”
The immune epitope database (IEDB) 3.0 (2014, background): “Examples include: use of NCBI Taxonomy (4) to describe organisms, GenBank (5) and UniProt (6) for proteins, ChEBI (3) for non-peptidic structures, the Ontology for Biomedical Investigations (OBI) (7) for assay types, Gazetteer (http: //purl.bioontology.org/ontology/GAZ) for geographic location and t”
DISEASES: Text mining and data integration of disease–gene associations (2014, methodology): “To construct a dictionary of diseases for use in NER, we extracted all names and synonyms from the Disease Ontology [15].”