Data integration: a theoretical perspective
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IEEE Internet Computing
TAMBIS: Transparent Access to Multiple Bioinformatics Information Sources
ISMB '98 Proceedings of the 6th International Conference on Intelligent Systems for Molecular Biology
A reference ontology for biomedical informatics: the foundational model of anatomy
Journal of Biomedical Informatics - Special issue: Unified medical language system
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IBM Systems Journal - Deep computing for the life sciences
Analyzing theme, space, and time: an ontology-based approach
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
A semantic web approach to biological pathway data reasoning and integration
Web Semantics: Science, Services and Agents on the World Wide Web
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
An ontological approach to the document access problem of insider threat
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Ontologies and Data Integration in Biomedicine: Success Stories and Challenging Issues
DILS '08 Proceedings of the 5th international workshop on Data Integration in the Life Sciences
Guest Editorial: Semantic mashup of biomedical data
Journal of Biomedical Informatics
Triplify: light-weight linked data publication from relational databases
Proceedings of the 18th international conference on World wide web
Information integration in bioinformatics with ontologies and standards
Semantic techniques for the web
An ontological modeling approach to cerebrovascular disease studies: The NEUROWEB case
Journal of Biomedical Informatics
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Objectives: This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. Methods: We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Results: Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Conclusion: Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. Resource page: http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/