This article is part of the supplement: Semantic e-Science in BiomedicineAdvancing translational research with the Semantic Web1Millennium 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
BMC Bioinformatics 2007, 8(Suppl 3):S2doi:10.1186/1471-2105-8-S3-S2
AbstractBackgroundA 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. ResultsWe 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. ConclusionSemantic 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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