Swedish Research Council, Swedish State Support for Clinical Research, Alzheimer's Association, Knut and Alice Wallenberg Foundation, Torsten Söderberg Foundation, Alzheimer Foundation (Sweden), European Research Council, and Biomedical Research Forum.

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.

Alzheimer's disease is a genetically complex disorder; rare variants in the triggering receptor expressed on myeloid cells 2 (TREM2) gene have been shown to as much as triple an individual's risk of developing Alzheimer's disease. TREM2 is a transmembrane receptor expressed in cells of the myeloid lineage, and its association with Alzheimer's disease supports the involvement of immune and inflammatory pathways in the cause of the disease, rather than as a consequence of the disease. TREM2 variants associated with Alzheimer's disease induce partial loss of function of the TREM2 protein and alter the behaviour of microglial cells, including their response to amyloid plaques. TREM2 variants have also been shown to cause polycystic lipomembranous osteodysplasia with sclerosing leukoencephalopathy and frontotemporal dementia. Although the low frequency of TREM2 variants makes it difficult to establish robust genotype-phenotype correlations, such studies are essential to enable a comprehensive understanding of the role of TREM2 in different neurological diseases, with the ultimate goal of developing novel therapeutic approaches.

Developing cures for highly complex diseases, such as neurodegenerative disorders, requires extensive interdisciplinary collaboration and exchange of biomedical information in context. Our ability to exchange such information across sub-specialties today is limited by the current scientific knowledge ecosystem's inability to properly contextualize and integrate data and discourse in machine-interpretable form. This inherently limits the productivity of research and the progress toward cures for devastating diseases such as Alzheimer's and Parkinson's. SWAN (Semantic Web Applications in Neuromedicine) is an interdisciplinary project to develop a practical, common, semantically structured, framework for biomedical discourse initially applied, but not limited, to significant problems in Alzheimer Disease (AD) research. The SWAN ontology has been developed in the context of building a series of applications for biomedical researchers, as well as in extensive discussions and collaborations with the larger bio-ontologies community. In this paper, we present and discuss the SWAN ontology of biomedical discourse. We ground its development theoretically, present its design approach, explain its main classes and their application, and show its relationship to other ongoing activities in biomedicine and bio-ontologies.

Despite high prevalence of CHB in China, our study shows knowledge is limited and there is significant societal and internalized stigma associated with HBV infection.

Background: Neuroscientists often need to access a wide range of data sets distributed over the Internet. These data sets, however, are typically neither integrated nor interoperable, resulting in a barrier to answering complex neuroscience research questions. Domain ontologies can enable the querying heterogeneous data sets, but they are not sufficient for neuroscience since the data of interest commonly span multiple research domains. To this end, eNeuroscience seeks to provide an integrated platform for neuroscientists to discover new knowledge through seamless integration of the very diverse types of neuroscience data. Here we present a Semantic Web approach to building this e-Neuroscience framework by using the Resource Description Framework (RDF) and its vocabulary description language, RDF Schema (RDFS), as a standard data model to facilitate both representation and integration of the data.

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