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AMIA Summits Transl Sci Proc. 2012; 2012: 25–34.
Published online Mar 19, 2012.
PMCID: PMC3392061
A Bayesian Translational Framework for Knowledge Propagation, Discovery, and Integration Under Specific Contexts
Michelle Deng,1* Amin Zollanvari,1,2* and Gil Alterovitz1,2,3
1Children’s Hospital Informatics Program at Harvard-MIT Division of Health Science, Boston, MA;
2 Center for Biomedical Informatics, Harvard Medical School, Boston, MA;
3 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
*These co-authors contributed equally to this work.
Abstract
The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts—rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well.
Articles from AMIA Summits on Translational Science Proceedings are provided here courtesy of
American Medical Informatics Association