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. (Request a copy)

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 Applied Sciences > Department of Pharmacy Health and Wellbeing
Related URLs:
Depositing User: Sheila Garfield
Date Deposited: 09 Oct 2015 08:36
Last Modified: 09 Oct 2015 08:36
URI: http://sure.sunderland.ac.uk/id/eprint/5767

Actions (login required)

View Item View Item