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    Protein data modelling for concurrent sequential patterns

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    Authors
    Lu, Jing
    Keech, Malcolm
    Wang, Cuiqing
    Affiliation
    University of Bedfordshire
    Issue Date
    2014-09
    Subjects
    protein sequences
    data mining
    concurrent sequential patterns (ConSP)
    bioinformatics
    ConSP modelling
    biological databases
    knowledge representation
    visualization
    
    Metadata
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    Abstract
    Protein sequences from the same family typically share common patterns which imply their structural function and biological relationship. The challenge of identifying protein motifs is often addressed through mining frequent itemsets and sequential patterns, where post-processing is a useful technique. Earlier work has shown that Concurrent Sequential Patterns mining can be applied in bioinformatics, e.g. to detect frequently occurring concurrent protein sub-sequences. This paper presents a companion approach to data modelling and visualisation, applying it to real-world protein datasets from the PROSITE and NCBI databases. The results show the potential for graph-based modelling in representing the integration of higher level patterns common to all or nearly all of the protein sequences.
    Citation
    Lu, J., Keech, M., Wang, C., (2014) 'Protein Data Modelling for Concurrent Sequential Patterns' 5th International Workshop on Biological Knowledge Discovery and Data Mining, Munich 3rd September.
    Publisher
    DEXA
    URI
    http://hdl.handle.net/10547/334492
    Additional Links
    http://www.dexa.org/previous/dexa2014/ws_program387a.html?cid=439
    Type
    Conference papers, meetings and proceedings
    Language
    en
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

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