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.