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CBMS
2007
IEEE

Auto-Extraction, Representation and Integration of a Diabetes Ontology Using Bayesian Networks

9 years 2 months ago
Auto-Extraction, Representation and Integration of a Diabetes Ontology Using Bayesian Networks
This paper describes how high level biological knowledge obtained from ontologies such as the Gene Ontology (GO) can be integrated with low level information extracted from a Bayesian network trained on protein interaction data. We can automatically generate a biological ontology by text mining the type II diabetes research literature. The ontology is populated with the entities and relationships from protein-to-protein interactions. New, previously unrelated information is extracted from the growing body of research literature and incorporated with knowledge already known on this subject from the gene ontology and databases such as BIND and BioGRID. We integrate the ontology within the probabilistic framework of Bayesian networks which enables reasoning and prediction of protein function.
Kenneth McGarry, Sheila Garfield, Stefan Wermter
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CBMS
Authors Kenneth McGarry, Sheila Garfield, Stefan Wermter
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