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{bio,medical} informatics

Wednesday, November 14, 2001

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find related articles. powered by google. Stanford Medical Informatics Preprint Archive Automating Data Acquisition into Ontologies from Pharmacogenetics Relational Data Sources Using Declarative Object Definitions and XML

"Ontologies are desirable for building knowledge bases that can perform inference over the breadth of genetic and clinical knowledge in domains such as pharmacogenomics. But because ontologies change and evolve, it is time consuming to maintain stable connections with external data sources that are in relational format. A method for interfacing ontology models with data acquisition from external relational data sources is proposed. This method uses a declarative interface between the ontology and the data source, modeled in the ontology and implemented using XML schema. Data is imported from the relational source into the ontology using XML that is validated using an XML schema. We have implemented this approach in PharmGKB (, a pharmacogenetics knowledge base we are developing. Our goals were to (1) import genetic sequence data, collected in relational format, into the pharmacogenetics ontology, and (2) automate the process of updating the links between the ontology and data acquisition when the ontology changes. We tested our approach by linking PharmGKB with data acquisition from a relational model of genetic sequence data. The ontology subsequently evolved, and we were able to rapidly update our interface with the external data and continue acquiring the data. Similar approaches may be helpful for integrating other heterogeneous information sources in order make the diversity of pharmacogenetics data amenable to computational analysis."

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Bioinformatics will be at the core of biology in the 21st century. In fields ranging from structural biology to genomics to biomedical imaging, ready access to data and analytical tools are fundamentally changing the way investigators in the life sciences conduct research and approach problems. Complex, computationally intensive biological problems are now being addressed and promise to significantly advance our understanding of biology and medicine. No biological discipline will be unaffected by these technological breakthroughs.


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