NCI Thesaurus: A semantic model integrating cancer-related clinical and molecular information

Sioutos, Nicholas; Coronado, Sherri de; Haber, Margaret W.; Hartel, Frank W.; Shaiu, Wen-Ling; Wright, Lawrence W.;

Open Access

doi: 10.1016/j.jbi.2006.02.013

Published: 01 Feb 2007 Journal: Journal of Biomedical Informatics, volume 40, issue 1, pages 30-43 (issn: 1532-0464,

Publisher: Elsevier BV

- Summary
- References
  (62)

Abstract

Over the last 8 years, the National Cancer Institute (NCI) has launched a major effort to integrate molecular and clinical cancer-related information within a unified biomedical informatics framework, with controlled terminology as its foundational layer. The NCI Thesaurus is the reference terminology underpinning these efforts. It is designed to meet the growing need for accurate, comprehensive, and shared terminology, covering topics including: cancers, findings, drugs, therapies, anatomy, genes, pathways, cellular and subcellular processes, proteins, and experimental organisms. The NCI Thesaurus provides a partial model of how these things relate to each other, responding to actual user needs and implemented in a deductive logic framework that can help maintain the integrity and extend the informational power of what is provided. This paper presents the semantic model for cancer diseases and its uses in integrating clinical and molecular knowledge, more briefly examines the models and uses for drug, biochemical pathway, and mouse terminology, and discusses limits of the current approach and directions for future work.

Subjects by Vocabulary

ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION

Microsoft Academic Graph classification: Data science Computer science System integration business.industry business Deductive reasoning Semantics Semantic data model Terminology Vocabulary media_common.quotation_subject media_common MEDLINE Health informatics

Subjects

Health Informatics, Computer Science Applications

62 references, page 1 of 7

[1] Hartel FW, De Coronado S. Information standards within the National Cancer Institute. In: Silva JS, Ball MJ, Chute CG, Douglas JV, Langlotz C, Niland J, Scherlis W, editors. Cancer informatics: essential technologies for clinical trials. Berlin: Springer; 2002. p. 135-56.

[2] Hubbard SL. Information systems in oncology. In: Devita VT, Hellman S, Rosenberg SA, editors. Cancer: Principles & Practice of Oncology. 6th ed. Philadelphia: Lippincott Williams & Wilkins; 2001. p. 3135-46.

[3] caBIG. Available at: <http://cabig.nci.nih.gov/>. Accessed October 14, 2004.

[4] Sittig DF. Grand challenges in medical informatics. J Am Med Inform Assoc 1994;1(5):412-3. [OpenAIRE]

[5] Lindberg DAB, Humphreys BL, McCray AT. The unified medical language system. Meth Inf Med 1993;32:281-91. [OpenAIRE]

[6] U.S. Department of Health & Human Services. Federal Health Architecture. <http://www.hhs.gov/fedhealtharch/>. Accessed October 14, 2004.

[7] Rector A, Nowlan W, Glowinski A. Goals for concept representation in the GALEN project. In: Proc Annu Symp Comput Appl Med Care 1993:414-18. [OpenAIRE]

[8] SNOMED International, College of American Pathologists. SNOMED CT. Available from: <http://www.snomed.org/snomedct/index.html/>. Accessed February 23, 2005.

[9] Bard JB, Rhee SY. Ontologies in biology: design, applications and future challenges. Nat Rev Genet 2004;5(3):213-22.

[10] Covitz P, Hartel FW, Schaefer C, de Coronado S, Fragoso G, Sahni H, et al. caCORE: A common infrastructure for cancer informatics. Bioinformatics 2003;19:2402-12.

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