Semantic Web for Health Care and Life Sciences: a review of the state of the art.

Biomedical researchers need to be able to ask questions that span many heterogeneous data sources in order to make well-informed decisions that may lead to important scientific breakthroughs. For this to be achieved, diverse types of data about drugs, patients, diseases, proteins, cells, pathways and so on must be effectively integrated. Yet, linking disparate biomedical data continues to be a challenge due to inconsistency in naming and heterogeneity in data models and formats. Many organizations are now exploring the use of Semantic Web technologies in the hope of easing the cost of data integration [1]. The benefits promised by the Semantic Web include integration of heterogeneous data using explicit semantics, simplified annotation and sharing of findings, rich explicit models for data representation, aggregation and search, easier re-use of data in unanticipated ways, and the application of logic to infer additional information [2]. The World Wide Web Consortium (W3C) (http://www.w3.org/) has established the Semantic Web for Health Care and Life Sciences Interest Group (HCLS IG) (http://www.w3.org/2001/sw/ hcls/) to help organizations in their adoption of the Semantic Web. The HCLS IG is chartered to develop and support the use of Semantic Web technologies to improve collaboration, research and development, innovation, and adoption in the domains of Health Care and Life Sciences. As a part of realizing this vision, a workshop on the Semantic Web for Health Care and Life Sciences was organized in conjunction with WWW2008 (http://esw.w3.org/topic/HCLS/WWW2008) [3]. The workshop provided a review of the latest positions and research in this domain. Five of the seven papers within this issue originated from the HCLS/WWW2008 workshop and review a range of Semantic Web technologies/approaches employed in different biomedical domains. Vandervalk etal. describe ‘The State of the Union’ for the adoption of Semantic Web standards by key institutes in bioinformatics. The paper explores the nature and connectivity of several communitydriven semantic warehousing projects. It reports on the progress with the CardioSHARE/Moby-2 project, which aims to make the resources of the ‘Deep Web’ transparently accessible through SPARQL queries. It points out that the warehouse approach is limited, in that queries are confined to the resources that have been selected for inclusion. It also discusses a related problem that the majority of bioinformatics data exist in the ‘Deep Web’, that is, the data does not exist until an application or analytical tool is invoked, and therefore does not have a predictable Web address. It also highlights that the inability to utilize Uniform Resource Identifiers (URIs) to address bioinformatics data is a barrier to its accessibility in the Semantic Web. Das et al. discuss the use of ontologies to bridge diverse Web-based communities. The paper introduces the Science Collaboration Framework (SCF) as a reusable platform for advanced online collaboration in biomedical research. SCF supports structured Web 2.0 community discourse amongst researchers, makes heterogeneous data resources available to collaborating scientists, captures the semantics of the relationships between resources, and structures discourse around the resources. The first instance of the SCF framework is being used to create an openaccess online community for stem cell research— StemBook (http://www.stembook.org). The SCF framework has been applied to interdisciplinary areas such as neurodegenerative disease and neuro-repair research, but has broad utility across the natural sciences. Zhao et al. describe various design patterns for representing and querying provenance information relating to mapping links between heterogeneous data from sources in the domain of functional genomics. The paper illustrates the use of named RDF graphs at different levels of granularity to make provenance assertions about linked data. It also demonstrates that these assertions are sufficient to support requirements including data currency, integrity, evidential support and historical queries. BRIEFINGS IN BIOINFORMATICS. VOL 10. NO 2. 111^113 doi:10.1093/bib/bbp015