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This article is part of the supplement: Semantic e-Science in Biomedicine

Open AccessHighly AccessMethodology

Advancing translational research with the Semantic Web

Alan Ruttenberg1 email, Tim Clark2 email, William Bug3 email, Matthias Samwald4 email, Olivier Bodenreider5 email, Helen Chen6 email, Donald Doherty7 email, Kerstin Forsberg8 email, Yong Gao9 email, Vipul Kashyap10 email, June Kinoshita11 email, Joanne Luciano12 email, M Scott Marshall13 email, Chimezie Ogbuji14 email, Jonathan Rees15 email, Susie Stephens16 email, Gwendolyn T Wong11 email, Elizabeth Wu11 email, Davide Zaccagnini17 email, Tonya Hongsermeier10 email, Eric Neumann18 email, Ivan Herman19 email and Kei-Hoi Cheung20 email

1Millennium Pharmaceuticals, Cambridge, MA, USA

2Initiative in Innovative Computing, Harvard University, Cambridge, MA, USA

3Laboratory for Bioimaging and Anatomical Informatics, Department of Neurobiology and Anatomy, Drexel University College of Medicine, Philadelphia, PA, USA

4Section on Medical Expert and Knowledge-Based Systems, Medical University of Vienna, Vienna, Austria

5National Library of Medicine, Bethesda, MD, USA

6Agfa Healthcare, Waterloo, Ontario, Canada

7Brainstage Research, Pittsburgh, PA, USA

8AstraZeneca, Mölndal, Sweden

9MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Charlestown, MA, USA

10Partners HealthCare System, Wellesley, MA, USA

11Alzheimer Research Forum, Boston, MA, USA

12Harvard Medical School, Boston, MA, USA

13Integrative Bioinformatics Unit, University of Amsterdam, Amsterdam, The Netherlands

14Cleveland Clinic Foundation, Cleveland, OH, USA

15Science Commons, Cambridge, MA, USA

16Oracle, Burlington, MA, USA

17Language & Computing, Reston, VA, USA

18Teranode Corporation, Seattle, WA, USA

19World Wide Web Consortium (W3C)

20Center for Medical Informatics, Yale University School of Medicine, New Haven, CT, USA

author email corresponding author email

BMC Bioinformatics 2007, 8(Suppl 3):S2doi:10.1186/1471-2105-8-S3-S2

Published: 9 May 2007

Abstract

Background

A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen Translational Research, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.

Results

We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine.

Conclusion

Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.


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