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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

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

McGarry, Kenneth, Garfield, Sheila and Wermter, Stefan (2007) Auto-Extraction, Representation and Integration of a Diabetes Ontology Using Bayesian Networks. In: Twentieth IEEE International Symposium on Computer-Based Medical Systems, 20-22 June 2007, Maribor, Slovenia.

Item Type: Conference or Workshop Item (Paper)


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.

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Depositing User: Sheila Garfield


Item ID: 5767
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ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 09 Oct 2015 08:36
Last Modified: 18 Dec 2019 15:38


Author: Kenneth McGarry ORCID iD
Author: Sheila Garfield
Author: Stefan Wermter

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence

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