• DocumentCode
    2723319
  • Title

    Biological Sequence Mining Using Plausible Neural Network and its Application to Exon/intron Boundaries Prediction

  • Author

    Li, Kuochen ; Chang, Dar-Jen ; Rouchka, Eric ; Yuan Yan Chen

  • Author_Institution
    CECS, Louisville Univ., KY
  • fYear
    2007
  • fDate
    1-5 April 2007
  • Firstpage
    165
  • Lastpage
    169
  • Abstract
    Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, plausible neural network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence mining. The specific task of biological sequence mining, exon/intron prediction, is implemented by using PNN. The experimental results show the capability of solving biological sequence mining tasks using PNN
  • Keywords
    biology computing; data analysis; data mining; neural nets; pattern classification; pattern clustering; biological sequence mining; exon/intron boundaries prediction; large dataset analysis; pattern classification; pattern clustering; pattern discovery; plausible neural network; Biological information theory; Biological system modeling; Biology computing; Computational biology; Computer architecture; Mutual information; Neural networks; Neurons; Predictive models; Sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Bioinformatics and Computational Biology, 2007. CIBCB '07. IEEE Symposium on
  • Conference_Location
    Honolulu, HI
  • Print_ISBN
    1-4244-0710-9
  • Type

    conf

  • DOI
    10.1109/CIBCB.2007.4221219
  • Filename
    4221219