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

Integration of Hybrid Bio-Ontologies using Bayesian Networks for Knowledge Discovery

McGarry, Kenneth, Garfield, Sheila, Morris, Nick and Wermter, Stefan (2007) Integration of Hybrid Bio-Ontologies using Bayesian Networks for Knowledge Discovery. In: Third International Workshop on Neural-Symbolic Learning and Reasoning, 6-12 January 2007, Hyderabad, India.

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 net-
work 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 in-
corporated 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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More Information

Depositing User: Sheila Garfield


Item ID: 5769
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 09 Oct 2015 14:17
Last Modified: 18 Dec 2019 15:38


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

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence

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