Clinical ontologies for discovery applications

Yves A. Lussier, Olivier Bodenreider

Research output: Chapter in Book/Report/Conference proceedingChapter

13 Scopus citations


The recent achievements in the Human Genome Project have made possible a high-throughput systems approach for accelerating bioinformatics research. In addition, the NIH Whole Genome Association Studies will soon supply abundant clinical data annotated to clinical ontologies for mining. The elucidation of the molecular underpinnings of human diseases will require the use of genomic and ontology-anchored clinical databases. The objective of this chapter is to provide the background required to conduct biological discovery research with clinical ontologies. We first provide a description of the complexity of clinical information and the main characteristics of various clinical ontologies. The second section illustrates several methods used to integrate clinical ontologies and therefore databases annotated with heterogeneous standards. Finally the third section reviews a few genome-wide studies that leverage clinical ontologies. We conclude with the future opportunities and challenges offered by the Semantic Web and clinical ontologies for clinical data integration and mining. Discovery research faces the challenge of generating novel tools to help collect, access, integrate, organize and manage clinical information and enable genome wide analyses to associate phenotypic information with genomic data at different scales of biology. Collaborations between bioinformaticians and clinical informaticians are poised to leverage the Semantic Web.

Original languageEnglish (US)
Title of host publicationSemantic Web
Subtitle of host publicationRevolutionizing Knowledge Discovery in the Life Sciences
PublisherSpringer US
Number of pages19
ISBN (Electronic)9780387484389
ISBN (Print)0387484361, 9780387484365
StatePublished - 2007


  • Clinical Ontology
  • Clinical Phenotypes
  • Clinical Terminology
  • Discovery
  • Phenomics

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)
  • Computer Science(all)


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