AMDCC Ontologies
Workshop on Clinical Trial Ontologies

Papers:

A Hybrid Approach for Developing an Ontology of Genetic Susceptibility to Common Disease (OGSCD)
Yu Lin; Sakamoto, N.
BioMedical Engineering and Informatics, 2008. BMEI 2008. International Conference on
Volume 1, Issue , 27-30 May 2008 Page(s):321 - 326
Digital Object Identifier   10.1109/BMEI.2008.338

Summary:
Motivation: To identify the genetic susceptibility to human disease is the central task for the medical genetics researchers; however, to distinguish the true susceptibility factors remains far less satisfaction. Considering the complex and unstructured information on this topic, an ontology is important to explicit the concepts and their relationships in the domain of the genetic susceptible factors to common disease. Here by using Diabetes Mellitus as an example,we developed an Ontology of Genetic Susceptibility to Common Disease (OGSCD). Results: The core conception was primarily summarized, then we half adopted conceptions from BFO to develop the ontology starting from the top level. A hybrid of middle-out and top-down approach was conducted for the construction of OGSCD. By using Protege-OWL editor, we developed the ontology which represents the knowledge of the susceptibility genetic factors to common disease.

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Auto-Extraction, Representation and Integration of a Diabetes Ontology Using Bayesian Networks
McGarry, K.; Garfield, S.; Wermter, S.
Computer-Based Medical Systems, 2007. CBMS apos;07. Twentieth IEEE International Symposium on
Volume , Issue , 20-22 June 2007 Page(s):612 - 617
Digital Object Identifier   10.1109/CBMS.2007.26

Summary:
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.

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HIC 2002: Proceedings: Improving Quality by Lowering Barriers

An Ontology-driven Multi-agent Approach for Healthcare