The Translational Medicine Ontology and Knowledge Base: Driving Personalized Medicine By Bridging The Gap Between Bench And Bedside

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The Translational Medicine Ontology and Knowledge Base: Driving Personalized Medicine By Bridging The Gap Between Bench And Bedside

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Title: The Translational Medicine Ontology and Knowledge Base: Driving Personalized Medicine By Bridging The Gap Between Bench And Bedside
Author: Luciano, Joanne S; Andersson, Bosse; Batchelor, Colin; Bodenreider, Olivier; Denney, Christine K; Domarew, Christopher; Gambet, Thomas; Harland, Lee; Jentzsch, Anja; Kashyap, Vipul; Kos, Peter; Kozlovsky, Julia; Lebo, Timothy; Marshall, Scott M; McCusker, James P; McGuinness, Deborah L; Ogbuji, Chimezie; Pichler, Elgar; Samwald, Matthias; Schriml, Lynn; Whetzel, Patricia L; Stephens, Susie; Dumontier, Michel; Clark, Timothy William; Prud'hommeaux, Eric; Tonellato, Peter J; Zhao, Jun

Note: Order does not necessarily reflect citation order of authors.

Citation: Luciano, Joanne S, Bosse Andersson, Colin Batchelor, Olivier Bodenreider, Tim Clark, Christine K Denney, Christopher Domarew, and et al. 2011. The translational medicine ontology and knowledge base: driving personalized medicine by bridging the gap between bench and bedside. Journal of Biomedical Semantics 2(Suppl 2): S1.
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Abstract: Background: Translational medicine requires the integration of knowledge using heterogeneous data from health care to the life sciences. Here, we describe a collaborative effort to produce a prototype Translational Medicine Knowledge Base (TMKB) capable of answering questions relating to clinical practice and pharmaceutical drug discovery. Results: We developed the Translational Medicine Ontology (TMO) as a unifying ontology to integrate chemical, genomic and proteomic data with disease, treatment, and electronic health records. We demonstrate the use of Semantic Web technologies in the integration of patient and biomedical data, and reveal how such a knowledge base can aid physicians in providing tailored patient care and facilitate the recruitment of patients into active clinical trials. Thus, patients, physicians and researchers may explore the knowledge base to better understand therapeutic options, efficacy, and mechanisms of action. Conclusions: This work takes an important step in using Semantic Web technologies to facilitate integration of relevant, distributed, external sources and progress towards a computational platform to support personalized medicine. Availability: TMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.
Published Version: doi://10.1186/2041-1480-2-S2-S1
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3102889/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:5142083

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