Dramatic advances in the development of biomedical ontologies hold the promise of a deeper and clearer understanding of the molecular and genetic aspects that affect human health. Biomedical data and knowledge stored in ontologies and databases have the potential to empower researchers in the life sciences to access and find conclusive evidence that can be translated to medical diagnosis and treatment. Enormous effort is being expended to create suites of interoperable ontologies that can encompass the life sciences. However, the extensive knowledge codifications created and curated by the developers of existing ontologies rarely interact with the beliefs and hypotheses postulated by other researchers. This inability to make use of codified and established knowledge hinders the ability of researchers to take advantage of the capabilities afforded by Semantic Web technologies in terms of computational reasoning. In this project, we propose to develop the GeneBel system as a software solution to allow researchers in biology and genetics to postulate hypotheses, and to test and verify these hypotheses against the body of knowledge existing in multiple interconnected ontologies. At the core of the proposed GeneBel system, a belief and hypothesis encoding mechanism permits the creation of hypotheses as belief assertions, an ontology generation and alignment component allows the interconnection of multiple ontologies and data sources, and a process of hypothesis verification finds ontology assertions that either corroborate or contradict hypotheses. In Phase I of the project, the specific belief encoding techniques and underlying reasoning and hypothesis verification methods will be implemented in a prototype solution and tested against a set of predefined research scenarios and hypotheses. During Phase II, the complete GeneBel system will be constructed and evaluated in the execution of real-world hypothesis verification in genetics and biomedical research.

Public Health Relevance

The GeneBel System is a software solution for the management of knowledge and verification of hypotheses focused on genetics research. It uses ontology and computational reasoning methods to create semantic encodings of beliefs, and to find ontology assertions that corroborate or contradict these beliefs.

National Institute of Health (NIH)
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
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Special Emphasis Panel (ZRG1)
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Ravichandran, Veerasamy
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Infotech Soft, Inc.
United States
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