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.

Full text not available from this repository.

Search Google Scholar

Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computing > Artificial Intelligence
Computing
Divisions: Faculty of Technology
Faculty of Technology > School of Computer Science
Related URLs:
Depositing User: Sheila Garfield
Date Deposited: 09 Oct 2015 08:36
Last Modified: 18 Dec 2019 15:38
URI: http://sure.sunderland.ac.uk/id/eprint/5767
ORCID for Kenneth McGarry: ORCID iD orcid.org/0000-0002-9329-9835

Actions (login required)

View Item View Item