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    Integration of protein data sources through PO

    20180_downloaded_stream_168.pdf (262.9Kb)
    Access Status
    Open access
    Authors
    Sidhu, Amandeep
    Dillon, Tharam S.
    Chang, Elizabeth
    Date
    2006
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Sidhu, Amandeep and Dillon, Tharam and Chang, Elizabeth. 2006. : Integration of protein data sources through PO, in Bressan, S. and Kung, J. and Wagner, R. (ed), International Conference on Database and Expert Systems Applications (DEXA), Sep 04 2006, pp. 519-527. Krakow, Poland: Springer-Verlag.
    Source Title
    Lecture Notes in Computer Science (LNCS-4080): 17th International Conference on Database and Expert Systems Applications (DEXA)
    Source Conference
    International Conference on Database and Expert Systems Applications (DEXA)
    DOI
    10.1007/11827405_51
    Faculty
    Curtin Business School
    School
    Centre for Extended Enterprises and Business Intelligence
    Remarks

    The original publication is available at https://link.springer.com/

    URI
    http://hdl.handle.net/20.500.11937/20823
    Collection
    • Curtin Research Publications
    Abstract

    Resolving heterogeneity among various protein data sources is a crucial problem if we want to gain more information about proteomics process. Information from multiple protein databases like PDB, SCOP, and UniProt need to integrated to answer user queries. Issues of Semantic Heterogeneity haven?t been addressed so far in Protein Informatics. This paper outlines protein data source composition approach based on our existing work of Protein Ontology (PO). The proposed approach enables semi-automatic interoperation among heterogeneous protein data sources. The establishment of semantic interoperation over conceptual framework of PO enables us to get a better insight on how information can be integrated systematically and how queries can be composed. The semantic interoperation between protein data sources is based on semantic relationships between concepts of PO. No other such generalized semantic protein data interoperation framework has been considered so far.

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