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This article is part of the supplement: Proceedings of the Bio-Ontologies Special Interest Group Meeting 2010

Open Access Highly Accessed Proceedings

The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside

Joanne S Luciano12*, Bosse Andersson3, Colin Batchelor4, Olivier Bodenreider5, Tim Clark67, Christine K Denney8, Christopher Domarew9, Thomas Gambet10, Lee Harland11, Anja Jentzsch12, Vipul Kashyap13, Peter Kos6, Julia Kozlovsky14, Timothy Lebo1, Scott M Marshall1516, James P McCusker1, Deborah L McGuinness1, Chimezie Ogbuji17, Elgar Pichler18, Robert L Powers2, Eric Prud’hommeaux10, Matthias Samwald192021, Lynn Schriml22, Peter J Tonellato6, Patricia L Whetzel23, Jun Zhao24, Susie Stephens25 and Michel Dumontier26*

Author Affiliations

1 Rensselaer Polytechnic Institute, Troy, NY, USA

2 Predictive Medicine Inc., Belmont, MA, USA

3 AstraZeneca, Lund, Sweden

4 Royal Society of Chemistry, Cambridge, UK

5 National Library of Medicine, Bethesda, MD, USA

6 Harvard Medical School, Boston, MA, USA

7 University of Manchester, Manchester UK

8 Eli Lilly and Company, Indianapolis, IN, USA

9 Albany Medical Center, Albany, NY, USA

10 W3C, Cambridge, MA, USA

11 Pfizer, Sandwich, UK

12 Freie Universität, Berlin, Germany

13 Cigna, Hartford, CT, USA

14 AstraZeneca, Waltham, MA, USA

15 Leiden University Medical Center, Leiden, NL

16 University of Amsterdam, Amsterdam, NL

17 Case Western Reserve University School of Medicine, Cleveland, OH, USA

18 W3C HCLSIG, W3C, Cambridge, MA, USA

19 Medical University of Vienna, Vienna, Austria

20 Information Retrieval Facility (IRF), Vienna, Austria

21 Digital Enterprise Research Institute (DERI), National University of Ireland Galway, Ireland

22 University of Maryland, Institute for Genome Sciences

23 Stanford University, Stanford, CA, USA

24 University of Oxford, Oxford, UK

25 Johnson & Johnson Pharmaceutical Research & Development L.L.C., Radnor, PA, USA

26 Carleton University, Ottawa, Canada

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Journal of Biomedical Semantics 2011, 2(Suppl 2):S1  doi:10.1186/2041-1480-2-S2-S1

Published: 17 May 2011

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 webcite and TMKB can be accessed at http://tm.semanticscience.org/sparql webcite.