• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Exploring potential discriminatory information embedded in PSSM to enhance protein structural class prediction accuracy

    Author(s)
    Dehzangi, Iman
    Paliwal, Kuldip
    Lyons, James
    Sharma, Alok
    Sattar, Abdul
    Griffith University Author(s)
    Sattar, Abdul
    Paliwal, Kuldip K.
    Sharma, Alok
    Lyons, James
    Dehzangi, Iman
    Year published
    2013
    Metadata
    Show full item record
    Abstract
    Determining the structural class of a given protein can provide important information about its functionality and its general tertiary structure. In the last two decades, the protein structural class prediction problem has attracted tremendous attention and its prediction accuracy has been significantly improved. Features extracted from the Position Specific Scoring Matrix (PSSM) have played an important role to achieve this enhancement. However, this information has not been adequately explored since the protein structural class prediction accuracy relying on PSSM for feature extraction still remains limited. In this study, ...
    View more >
    Determining the structural class of a given protein can provide important information about its functionality and its general tertiary structure. In the last two decades, the protein structural class prediction problem has attracted tremendous attention and its prediction accuracy has been significantly improved. Features extracted from the Position Specific Scoring Matrix (PSSM) have played an important role to achieve this enhancement. However, this information has not been adequately explored since the protein structural class prediction accuracy relying on PSSM for feature extraction still remains limited. In this study, to explore this potential, we propose segmentation-based feature extraction technique based on the concepts of amino acids' distribution and auto covariance. By applying a Support Vector Machine (SVM) to our extracted features, we enhance protein structural class prediction accuracy up to 16% over similar studies found in the literature. We achieve over 90% and 80% prediction accuracies for 25PDB and 1189 benchmarks respectively by solely relying on the PSSM for feature extraction.
    View less >
    Conference Title
    Lecture Notes in Computer Science: Pattern Recognition in Bioinformatics 8th IAPR International Conference Proceedings
    Publisher URI
    https://link.springer.com/book/10.1007/978-3-319-09192-1
    DOI
    https://doi.org/10.1007/978-3-642-39159-0_19
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/58763
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander