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Area of Science:

  • Biomedical Informatics
  • Ontology Engineering
  • Human Disease Research

Background:

  • The Human Disease Ontology (DO) was established in 2003 as a nomenclature and classification standard for human diseases.
  • Over 20 years, DO has become a crucial resource, integrating mechanistic disease drivers and expanding to over 11,300 diseases.
  • It currently links disease concepts via over 37,000 vocabulary cross-mappings (v2023-08-08).

Purpose of the Study:

  • Introduce the newly launched DO Knowledgebase (DO-KB).
  • Enhance the findability, accessibility, interoperability, and reusability (FAIR) of disease data.
  • Expand the DO's representation of the diseaseome and integrate semantic knowledge with external linked open data resources.

Main Methods:

  • Development of a new SPARQL service for querying disease data.
  • Implementation of a new Faceted Search Interface for improved data exploration.
  • Integration of the DO's semantic knowledge backbone with disease-related data from Open Linked Data resources.

Main Results:

  • The DO-KB provides an integrated data system built on the DO's semantic backbone.
  • Enhanced data accessibility and interoperability through new services and interfaces.
  • Expansion of disease representation and improved connectivity with external data sources.

Conclusions:

  • The DO-KB significantly advances the utility of the Human Disease Ontology.
  • It improves FAIR principles for disease data, fostering better research and data integration.
  • Ongoing efforts focus on assessing global impact and enhancing data quality.