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    Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks

    Access Status
    Fulltext not available
    Authors
    Wang, D.
    Lee, N.
    Dillon, Tharam S.
    Date
    2003
    Type
    Journal Article
    
    Metadata
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    Citation
    Wang, Dianhui and Lee, Nung and Dillon, Tharam S. 2003. Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural Networks. Neural Information Processing - Letters and Reviews. 1 (1): pp. 53-59.
    Source Title
    Neural Information Processing - Letters and Reviews
    ISSN
    17382572
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/31890
    Collection
    • Curtin Research Publications
    Abstract

    Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neural network approaches, while reasonably accurate at classification, give no information about the relationship between the unseen case and the classified items that is useful to biologist. In contrast, in this paper we use a generalized radial basis function (GRBF) neural network architecture that generates fuzzy classification rules that could be used for further knowledge discovery. Our proposed techniques were evaluated using protein sequences with ten classes of super-families downloaded from a public domain database, and the results compared favorably with other standard machine learning techniques.

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