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

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{MGMW07, 
 	 author =  {McGarry, Ken and Garfield, Sheila and Morris, Nick and Wermter, Stefan},  
 	 title = {Integration of Hybrid Bio-Ontologies using Bayesian Networks for Knowledge Discovery}, 
 	 booktitle = {Proceedings of the IJCAI-07 Third International Workshop on Neural-Symbolic Learning and Reasoning},
 	 editors = {Artur S. d'Avila Garcez, Pascal Hitzler, Guglielmo Tamburrini},
 	 number = {},
 	 volume = {},
 	 pages = {},
 	 year = {2007},
 	 month = {Jan},
 	 publisher = {},
 	 doi = {}, 
 }