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PMO: A knowledge representation model towards precision medicine.

Li Hou1, Meng Wu1, Hong Yu Kang1

  • 1Institute of Medical Information/Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China.

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Summary
This summary is machine-generated.

Precision Medicine Ontology (PMO) integrates scattered biomedical data using a semi-automated approach. This enhances knowledge discovery for applications like text mining and knowledge base construction in precision medicine.

Keywords:
biomedical ontologycontrolled vocabularyprecision medicinesemantic webtaxonomy

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Area of Science:

  • Biomedical Informatics
  • Knowledge Representation
  • Precision Medicine

Background:

  • Exponential growth of biomedical data in precision medicine (PM) necessitates effective data integration.
  • Valuable knowledge is often buried in scattered data, requiring formal representation of biomedical entities and relationships.
  • Existing knowledge representation models may lack comprehensive coverage for PM-specific entities and relationships.

Purpose of the Study:

  • To develop a formal knowledge representation model, the Precision Medicine Ontology (PMO), for integrating heterogeneous data in PM.
  • To address the selection of primary entities, integration of biomedical vocabularies, and definition of semantic relationships within PM.
  • To improve upon existing ontology development methods for a more comprehensive PM ontology.

Main Methods:

  • Proposed a semi-automated method, improving the Ontology Development 101 approach.
  • Defined the scope of PMO based on the definition of PM.
  • Collected and integrated terms from 62 biomedical vocabularies into the Precision Medicine Vocabulary (PMV) using a combination of machine and manual work.
  • Defined annotation properties, reused existing ontologies, and defined 93 semantic relationships.
  • Evaluated PMO and created a dedicated website for access.

Main Results:

  • The Precision Medicine Vocabulary (PMV) contains 4.53 million terms.
  • The PMO includes eleven branches of PM concepts (e.g., disease, gene, mutation, drug) and 93 semantic relationships.
  • PMO offers broader and deeper coverage of mutation, gene, and gene product compared to existing projects.

Conclusions:

  • The developed PMO is an open, extensible ontology for precision medicine.
  • PMO enriches the semantic types and vocabulary within the PM domain.
  • PMO benefits users in medical literature annotation, text mining, and knowledge base construction.