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    Towards a Methodology for Lipoprotein Ontology

    153492_153492.pdf (661.3Kb)
    Access Status
    Open access
    Authors
    Chen, Meifania
    Hadzic, Maja
    Date
    2010
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Chen, Meifania and Hadzic, Maja. 2010. Towards a Methodology for Lipoprotein Ontology, in Dillon, T. and Rubin, D. and Gallagher, W. and Sidhu, A. and Tsymbal, A. (ed), 23rd IEEE International Symposium on Computer-Based Medical Systems (CBMS 2010), Oct 12 2010, pp. 415-420. Perth, WA: IEEE.
    Source Title
    Proceedings of the 23rd IEEE international symposium on computer-based medical systems (CBMS 2010)
    Source Conference
    23rd IEEE International Symposium on Computer-Based Medical Systems (CBMS 2010)
    ISBN
    9781424491667
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    Copyright © 2010 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/4822
    Collection
    • Curtin Research Publications
    Abstract

    Abnormal plasma lipoprotein levels have been found to be significantly correlated to cardiovascular disease, the leading cause of mortality and morbidity worldwide. In addition, lipoprotein dysregulation, known as dyslipidemia, is a central feature in disease states such as diabetes and hypertension, which also increases the risk to cardiovascular disease. Despite progress in lipoprotein research, a vast number of the world population suffers from dyslipidemia. One of the major challenges that researchers face is the difficulties in accessing and integrating relevant information amidst massive quantities of heterogeneous data. Ontologies target these problems by providing a semantic framework of the concepts involved in a system of related instances to support systematic querying of information, data mining, as well as form the basis for collaboration between research teams. Lipoprotein Ontology will provide the basis for the design of various applications to enable interoperability between research groups or software agents, as well as the development of tools for the diagnosis and treatment of dyslipidemia. In this paper we present a nine-step methodology for the design of Lipoprotein Ontology. This methodology can be adapted for use in the design of other domain-specific ontologies.

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