Human Disease Ontology 2018 update: classification, content and workflow expansion

By L. Schriml, Elvira Mitraka, B. Tauber, Mike Schor, Lance Nickle, V. Felix, L. Jeng, C. Bearer, R. Lichenstein, Katharine Bisordi and 9 more. Nucleic Acids Res., 2018

455 citations recorded by the source corpus.

Abstract The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO’s knowledgebase has expanded the DO’s utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO’s user community since 2015. The DO’s continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6b9b8616cdb4b56acf410e73be54164f59897df6

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Citations in the corpus

Utilizing graph machine learning within drug discovery and development (2021, background): “Type of data Databases Acronym Drugs (structure, indications, targets) [117–121] Dr Drug combinations [122, 123] DC Protein (structure, sequence) [124] PS Protein interactions [125, 126] PI Gene annotations [127–129] GA Diseases [127, 128, 130, 131] Di”
Alliance of Genome Resources Portal: unified model organism research platform (2019, background): “Individual ribbon boxes are high level terms from the Disease Ontology (DO) (15).”
The DisGeNET knowledge platform for disease genomics: 2019 update (2019, methodology): “…attributes that cover a wide variety of biomedical resources: diseases are coded using UMLS ® (16) concept unique identifiers (CUIs), and annotated with the UMLS ® semantic type, the MeSH class, and the top level concepts from the Human Disease Ontology (17) and the Human Phenotype Ontology (18).”
FlyBase: a guided tour of highlighted features (2022, methodology): “Details of genetic models of human diseases are captured at the allele level, using controlled terms from the DO (Schriml et al. 2019).”
FlyBase: updates to the Drosophila melanogaster knowledge base (2020, background): “melanogaster-tohuman orthology relationships from DIOPT (6); (ii) human gene-to-phenotype relationships from OMIM (14); and (iii) OMIM phenotype cross-references within the Disease Ontology (DO) (15).”