We have extended our ealier work in capturing and analyzing chemical and biological entities to programmatically identifying important relationships between these various entities.  The programs further extract and model complex N-point associations between entities using Natural Language Processing techniques thus establishing a framework that allows scaling multiple analytics applications over extracted data and metadata.  Two reasoning frameworks will be presented that have proven to generate useful, verifiable hypotheses in the P53 biology space.

Computer analysis of the scientific literature

248th National Meeting (2014)

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