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Title:
DO-KB: a semantic approach driving mechanistic-based modeling to advance disease knowledge

Authors:
Lynn Schriml, Claudia Sanchez-Beato Johnson, and James Allen Baron

Abstract:
The diagnostic complexity of complex diseases is challenged by phenotype and molecular variability. Modeling complex disease etiology, the Human Disease Ontology (DO), works to address this challenge, discerning and integrating the multiple contributing molecular, environmental and socioeconomic factors in a singular framework. Serving a vast user community since 2003 (> 400 biomedical resources across 45 countries), the DO’s continual content and classification expansion is driven by the ever-evolving disease knowledge ecosystem. The DO, a designated Global Core Biodata Resource (https://globalbiodata.org/), empowers disease data integration, standardization, and analysis across the interconnected web of biomedical information. The DO provides expertly curated, human-readable, and machine-actionable disease data within a comprehensive etiological-devised disease classification system, spanning genetic, infectious, cancer, environmental, complex, rare, and common diseases. The DO links disease concepts across authoritative biomedical resources with over 37,000 curated (exact, broad and narrow) mappings to reference clinical vocabularies. The Human Disease Ontology Knowledgebase (DO-KB, https://www.disease-ontology.org/), (DO-KB SPARQL service, Faceted Search Interface and advanced API service) established to enhance data discovery, delivers an integrated data system that exposes the DO’s semantic knowledge and connects disease-related data across Open Linked Data resources. The DO-KB team actively invites collaborations and strives to support community-driven projects - towards expanding the semantic representation of complex disease modeling, working together to enhance the understanding of mechanisms driving disease.