• DocumentCode
    1018536
  • Title

    Discovery of Structural and Functional Features in RNA Pseudoknots

  • Author

    Chen, Qingfeng ; Chen, Yi-Ping Phoebe

  • Author_Institution
    Fac. of Sci. & Technol., Deakin Univ., VIC
  • Volume
    21
  • Issue
    7
  • fYear
    2009
  • fDate
    7/1/2009 12:00:00 AM
  • Firstpage
    974
  • Lastpage
    984
  • Abstract
    An RNA pseudoknot consists of nonnested double-stranded stems connected by single-stranded loops. There is increasing recognition that RNA pseudoknots are one of the most prevalent RNA structures and fulfill a diverse set of biological roles within cells, and there is an expanding rate of studies into RNA pseudoknotted structures as well as increasing allocation of function. These not only produce valuable structural data but also facilitate an understanding of structural and functional characteristics in RNA molecules. PseudoBase is a database providing structural, functional, and sequence data related to RNA pseudoknots. To capture the features of RNA pseudoknots, we present a novel framework using quantitative association rule mining to analyze the pseudoknot data. The derived rules are classified into specified association groups regarding structure, function, and category of RNA pseudoknots. The discovered association rules assist biologists in filtering out significant knowledge of structure-function and structure-category relationships. A brief biological interpretation to the relationships is presented, and their potential correlations with each other are highlighted.
  • Keywords
    biocomputing; data mining; macromolecules; RNA molecules; RNA pseudoknotted structures; association rules; nonnested double-stranded stems; single-stranded loops; structure-category relationships; structure-function relationships; H-pseudoknot; Life and Medical Sciences; Pattern Recognition; PseudoBase; RNA pseudoknots; association rule mining; function; loop; partition.; stem; structure;
  • fLanguage
    English
  • Journal_Title
    Knowledge and Data Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1041-4347
  • Type

    jour

  • DOI
    10.1109/TKDE.2008.231
  • Filename
    4695830