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Integration of prostate cancer clinical data using an ontology.

TitleIntegration of prostate cancer clinical data using an ontology.
Publication TypeJournal Article
Year of Publication2009
AuthorsMin, H, Manion, FJ, Goralczyk, E, Wong, Y-N, Ross, E, J Beck, R
JournalJ Biomed Inform
Date Published2009 Dec
KeywordsComputational Biology, Database Management Systems, Databases, Factual, Humans, Information Storage and Retrieval, Male, Prostatic Neoplasms, Semantics, Terminology as Topic, User-Computer Interface

It is increasingly important for investigators to efficiently and effectively access, interpret, and analyze the data from diverse biological, literature, and annotation sources in a unified way. The heterogeneity of biomedical data and the lack of metadata are the primary sources of the difficulty for integration, presenting major challenges to effective search and retrieval of the information. As a proof of concept, the Prostate Cancer Ontology (PCO) is created for the development of the Prostate Cancer Information System (PCIS). PCIS is applied to demonstrate how the ontology is utilized to solve the semantic heterogeneity problem from the integration of two prostate cancer related database systems at the Fox Chase Cancer Center. As the results of the integration process, the semantic query language SPARQL is applied to perform the integrated queries across the two database systems based on PCO.

Alternate JournalJ Biomed Inform
PubMed ID19497389
PubMed Central IDPMC2784120
Grant ListP30 CA 06927 / CA / NCI NIH HHS / United States
P30 CA006927-41 / CA / NCI NIH HHS / United States
Frank Manion
University of Michigan Rogel Cancer Center at North Campus Research Complex
1600 Huron Parkway, Bldg 100, Rm 1004 
Mailing Address: 2800 Plymouth Rd, NCRC 100-1004
Ann Arbor, MI 48109-2800 
Ph. (734) 764-8848 Fax. (734) 615-0507

Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592. The content is solely the responsibility
of the authors and does not necessarily represent the official views of the
National Institutes of Health.

Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592 by the use of the following Cancer Center
Shared Resource(s): Biostatistics, Analytics & Bioinformatics; Flow Cytometry;
Transgenic Animal Models; Tissue and Molecular Pathology; Structure & Drug
Screening; Cell & Tissue Imaging; Experimental Irradiation; Preclinical
Imaging & Computational Analysis; Health Communications; Immune Monitoring;

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