Home > Research > Publications & Outputs > The translational medicine ontology and knowled...

Electronic data

  • 2041-1480-2-S2-S1

    Rights statement: © 2011 Luciano et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Final published version, 1 MB, PDF document

    Available under license: CC BY

Links

Text available via DOI:

View graph of relations

The translational medicine ontology and knowledge base: driving personalized medicine by bridging the gap between bench and bedside

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
  • Joanne S. Luciano
  • Bosse Andersson
  • Colin Batchelor
  • Olivier Bodenreider
  • Tim Clark
  • Christine K. Denney
  • Christopher Domarew
  • Thomas Gambet
  • Lee Harland
  • Anja Jentzsch
  • Jun Zhao
Close
Article numberS1
<mark>Journal publication date</mark>2011
<mark>Journal</mark>Journal of Biomedical Semantics
Issue numberSuppl 2
Volume2
Publication StatusPublished
<mark>Original language</mark>English

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.

Bibliographic note

© 2011 Luciano et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.