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- W2009174925 abstract "In biomedical applications, network models are commonly used to represent interactions and higher-level associations among biological entities. Integrated analyses of these interaction and association data has proven useful in extracting knowledge, and generating novel hypotheses for biomedical research. For example, integrated mining of clinical similarity among diseases, known disease-gene associations, and molecular interactions among proteins provide insight on prioritizing candidate disease genes. However, since most datasets provide their own schema and query interface, opportunities for exploratory and integrative querying of disparate data are currently limited. In this study, we capitalize on RDF-based representations of biomedical interaction and association data to develop a querying framework that enables efficient processing and flexible specification of graph template matching queries. The proposed framework enables integrative querying of biomedical databases to discover complex patterns of associations among a diverse range of biological entities, including biomolecules, biological processes, organisms, and phenotypes. Our experimental results on the UniProt dataset show the proposed framework can be used to efficiently process complex queries, and identify biologically relevant patterns of associations that cannot be readily obtained by querying each dataset independently." @default.
- W2009174925 created "2016-06-24" @default.
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- W2009174925 date "2015-09-09" @default.
- W2009174925 modified "2023-10-17" @default.
- W2009174925 title "Integrated querying of disparate association and interaction data in biomedical applications" @default.
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- W2009174925 doi "https://doi.org/10.1145/2808719.2808734" @default.
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