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Recent years have seen a huge increase in the amount of publicly-available information relevant to drug discovery, including online databases of compound and bioassay information; scholarly publications linking compounds with genes, targets and diseases; and predictive models that can suggest new links between compounds, genes, targets and diseases. However, there is a lack of tools and methods to integrate this information, and in particular to look for pertinent knowledge and relationships across multiple sources. At Indiana University we are tackling this problem by applying aggregative data mining tools and semantic web technologies including using an extensive web service infrastructure, RDF networks and inference engines, ontologies, and automated extraction of information from scholarly literature.
Date of Conference: Aug. 31 2010-Sept. 3 2010