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The DO-KB Knowledgebase: a 20-year journey developing the disease open science ecosystem

J. Allen Baron, Claudia Sanchez-Beato Johnson, Michael Schor, Dustin Olley, Lance Nickel, Victor Felix, James B. Munro, Susan M. Bello, Cynthia F. Bearer, Richard Lichenstein, Katharine Bisordi, Rima Koka, Carol L. Greene, Lynn M. Schriml

2023Nucleic Acids Research57 citationsDOIOpen Access PDF

Abstract

In 2003, the Human Disease Ontology (DO, https://disease-ontology.org/) was established at Northwestern University. In the intervening 20 years, the DO has expanded to become a highly-utilized disease knowledge resource. Serving as the nomenclature and classification standard for human diseases, the DO provides a stable, etiology-based structure integrating mechanistic drivers of human disease. Over the past two decades the DO has grown from a collection of clinical vocabularies, into an expertly curated semantic resource of over 11300 common and rare diseases linking disease concepts through more than 37000 vocabulary cross mappings (v2023-08-08). Here, we introduce the recently launched DO Knowledgebase (DO-KB), which expands the DO's representation of the diseaseome and enhances the findability, accessibility, interoperability and reusability (FAIR) of disease data through a new SPARQL service and new Faceted Search Interface. The DO-KB is an integrated data system, built upon the DO's semantic disease knowledge backbone, with resources that expose and connect the DO's semantic knowledge with disease-related data across Open Linked Data resources. This update includes descriptions of efforts to assess the DO's global impact and improvements to data quality and content, with emphasis on changes in the last two years.

Topics & Concepts

SPARQLInteroperabilityOntologyVocabularyComputer scienceDiseaseWorld Wide WebResource (disambiguation)Linked dataKnowledge representation and reasoningAnnotationSemantic WebBiologyData scienceRDFArtificial intelligenceMedicineLinguisticsPhilosophyEpistemologyPathologyComputer networkBiomedical Text Mining and OntologiesGenomics and Rare DiseasesBioinformatics and Genomic Networks
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