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AMIA Summits Transl Sci Proc. 2012; 2012: 79–86.
Published online Mar 19, 2012.
PMCID: PMC3392068
Context-Specific Ontology Integration: A Bayesian Approach
Kshitij Marwah,1,6 Dustin Katzin,1,2,3 Amin Zollanvari, PhD,1,4 Natalya F. Noy, PhD,5 Marco Ramoni, PhD,1* and Gil Alterovitz, PhD1,4,6
1 Children’s Hospital Informatics Program at Harvard-MIT Division of Health Science, Boston, MA;
2Department of Physics, Massachusetts Institute of Technology, Cambridge, MA;
3 Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA;
4 Center for Biomedical Informatics, Harvard Medical School, Boston, MA;
5 Stanford Center For Biomedical Informatics Research, Stanford CA;
6Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA;
*Deceased.
Abstract
We introduce a principled computational framework and methodology for automated discovery of context-specific functional links between ontologies. Our model leverages over disparate free-text literature resources to score the model of dependency linking two terms under a context against their model of independence. We identify linked terms as those having a significant bayes factor (p < 0.01). To scale our algorithm over massive ontologies, we propose a heuristic pruning technique as an efficient algorithm for inferring such links.
We have applied this method to translationalize Gene Ontology to all other ontologies available at National Center of Biomedical Ontology (NCBO) BioPortal under the context of Human Disease ontology. Our results show that in addition to broadening the scope of hypothesis for researchers, our work can potentially be used to explore continuum of relationships among ontologies to guide various biological experiments.
Articles from AMIA Summits on Translational Science Proceedings are provided here courtesy of
American Medical Informatics Association