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.

@InProceedings{MWG07, 
 	 author =  {McGarry, Ken and Wermter, Stefan and Garfield, Sheila},  
 	 title = {Auto Extraction, Representation and Integration of a Diabetes Ontology using Bayesian Networks}, 
 	 booktitle = {Proceedings of the IEEE International Symposium on Computer Based Medical Systems},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {612--617},
 	 year = {2007},
 	 month = {Jul},
 	 publisher = {IEEE},
 	 doi = {10.1109/CBMS.2007.26}, 
 }