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The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.

Publication ,  Journal Article
Sickle Cell Disease Ontology Working Group,
Published in: Database : the journal of biological databases and curation
January 2019

Sickle cell disease (SCD) is one of the most common monogenic diseases in humans with multiple phenotypic expressions that can manifest as both acute and chronic complications. Although described more than a century ago, challenges in comprehensive disease management and collaborative research on this disease are compounded by the complex molecular and clinical phenotypes of SCD, environmental and psychosocial factors, limited therapeutic options and ambiguous terminology. This ambiguous terminology has hampered the integration and interoperability of existing SCD knowledge, and SCD research translation. The SCD Ontology (SCDO), which is a community-driven integrative and universal knowledge representation system for SCD, overcomes this issue by providing a controlled vocabulary developed by a group of experts in both SCD and ontology design. SCDO is the first and most comprehensive standardized human- and machine-readable resource that unambiguously represents terminology and concepts about SCD for researchers, patients and clinicians. It is built around the central concept 'hemoglobinopathy', allowing inclusion of non-SCD haemoglobinopathies, such as thalassaemias, which may interfere with or influence SCD phenotypic manifestations. This collaboratively developed ontology constitutes a comprehensive knowledge management system and standardized terminology of various SCD-related factors. The SCDO will promote interoperability of different research datasets, facilitate seamless data sharing and collaborations, including meta-analyses within the SCD community, and support the development and curation of data-basing and clinical informatics in SCD.

Duke Scholars

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Published In

Database : the journal of biological databases and curation

DOI

EISSN

1758-0463

ISSN

1758-0463

Publication Date

January 2019

Volume

2019

Start / End Page

baz118

Related Subject Headings

  • Phenotype
  • Knowledge Bases
  • Humans
  • Biological Ontologies
  • Anemia, Sickle Cell
  • 4605 Data management and data science
  • 3102 Bioinformatics and computational biology
  • 0807 Library and Information Studies
  • 0804 Data Format
 

Citation

APA
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ICMJE
MLA
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Sickle Cell Disease Ontology Working Group, . (2019). The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation. Database : The Journal of Biological Databases and Curation, 2019, baz118. https://doi.org/10.1093/database/baz118
Sickle Cell Disease Ontology Working Group, Michael R. “The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.Database : The Journal of Biological Databases and Curation 2019 (January 2019): baz118. https://doi.org/10.1093/database/baz118.
Sickle Cell Disease Ontology Working Group. The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation. Database : the journal of biological databases and curation. 2019 Jan;2019:baz118.
Sickle Cell Disease Ontology Working Group, Michael R. “The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.Database : The Journal of Biological Databases and Curation, vol. 2019, Jan. 2019, p. baz118. Epmc, doi:10.1093/database/baz118.
Sickle Cell Disease Ontology Working Group. The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation. Database : the journal of biological databases and curation. 2019 Jan;2019:baz118.
Journal cover image

Published In

Database : the journal of biological databases and curation

DOI

EISSN

1758-0463

ISSN

1758-0463

Publication Date

January 2019

Volume

2019

Start / End Page

baz118

Related Subject Headings

  • Phenotype
  • Knowledge Bases
  • Humans
  • Biological Ontologies
  • Anemia, Sickle Cell
  • 4605 Data management and data science
  • 3102 Bioinformatics and computational biology
  • 0807 Library and Information Studies
  • 0804 Data Format