This article is part of the supplement: Proceedings of the Bio-Ontologies Special Interest Group Meeting 2010
Proceedings
The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside
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
Journal of Biomedical Semantics 2011, 2(Suppl 2):S1 doi:10.1186/2041-1480-2-S2-S1
Published: 17 May 2011Abstract
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.
