• 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
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • 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
  • Effective Statistical Features for Coding and Non-coding DNA Sequence Classification for Yeast, C. elegans and Human

    Author(s)
    Liew, AWC
    Wu, Y
    Yan, H
    Yan, H
    Yang, M
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2005
    Metadata
    Show full item record
    Abstract
    This study performs a quantitative evaluation of the different coding features in terms of their information content for the classification of coding and non-coding regions for three species. Our study indicated that coding features that are effective for yeast or C. elegans are generally not very effective for human, which has a short average exon length. By performing a correlation analysis, we identified a subset of human coding features with high discriminative power, but complementary in their information content. For this subset, a classification accuracy of up to 90% was obtained using a simple kNN classifier.This study performs a quantitative evaluation of the different coding features in terms of their information content for the classification of coding and non-coding regions for three species. Our study indicated that coding features that are effective for yeast or C. elegans are generally not very effective for human, which has a short average exon length. By performing a correlation analysis, we identified a subset of human coding features with high discriminative power, but complementary in their information content. For this subset, a classification accuracy of up to 90% was obtained using a simple kNN classifier.
    View less >
    Journal Title
    International Journal of Bioinformatics Research and Applications
    Volume
    1
    Issue
    2
    Publisher URI
    http://www.inderscience.com/ijbra
    DOI
    https://doi.org/10.1504/IJBRA.2005.007577
    Copyright Statement
    © 2005 Inderscience Publishers. Please refer to the journal's website for access to the definitive, published version.
    Subject
    Mathematical sciences
    Biological sciences
    Information and computing sciences
    Publication URI
    http://hdl.handle.net/10072/22081
    Collection
    • Journal articles

    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