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.

BackgroundTranslational 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.ResultsWe 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.ConclusionsThis 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.AvailabilityTMO can be downloaded from http://code.google.com/p/translationalmedicineontology and TMKB can be accessed at http://tm.semanticscience.org/sparql.

Semantic Web technologies offer the potential to revolutionize management of health care data by increasing interoperability and reusability while reducing the need for redundant data collection and storage. From 1998 through 2010, Cleveland Clinic sponsored a project designed to explore and develop this potential. The product of this effort, SemanticDB, is a suite of software tools and knowledge resources built to facilitate the collection, storage and use of the diverse data needed to conduct clinical research and health care quality reporting. SemanticDB consists of three main components: 1) a content repository driven by a meta-model that facilitates collection and integration of data in an XML format and automatically converts the data to RDF; 2) an inference-mediated, natural language query interface designed to identify patients who meet complex inclusion and exclusion criteria; and 3) a data production pipeline that uses inference to generate customized views of the repository content for statistical analysis and reporting. Since

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