Summary
A novel ontology based decision support framework and a development platform are described, which allow for the creation of global knowledge representation for local and personalised modelling and decision support. The main modules are: an ontology module; and a machine learning module. Both modules evolve through continuous learning from new data. Results from the machine learning procedures can be entered back to the ontology thus enriching its knowledge base and facilitating new discoveries. This framework supports global, local and personalised modelling. The latter is a process of model creation for a single person, based on their personal data and the information available in the ontology. Several methods for local and personalised modelling, both traditional and new, are described. A case study is presented on brain-gene-disease ontology, where a set of 12 genes related to central nervous system cancer are revealed from existing data and local profiles of patients are derived. Through ontology analysis, these genes are found to be related to different functions, areas, and other diseases of the brain. Two other case studies discussed in the paper are chronic disease ontology and risk evaluation, and cancer gene ontology and prognosis.
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Kasabov, N. et al. (2008). Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Computational Intelligence in Biomedicine and Bioinformatics. Studies in Computational Intelligence, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70778-3_4
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