• DocumentCode
    1267404
  • Title

    Comments on "parallel algorithms for finding a near-maximum independent set of a circle graph" [with reply]

  • Author

    Steeg, E.W. ; Takefuji, Y. ; Lee, Kuan-Chou

  • Author_Institution
    Dept. of Comput. Sci., Toronto Univ., Ont., Canada
  • Volume
    2
  • Issue
    2
  • fYear
    1991
  • fDate
    3/1/1991 12:00:00 AM
  • Firstpage
    328
  • Lastpage
    329
  • Abstract
    The authors refers to the work of Y. Takefuji et al. (see ibid., vol.1, pp. 263-267, Sept. (1990)), which is concerned with the problem of RNA secondary structure prediction, and draws the reader´s attention to his own model and experiments in training the neural networks on small tRNA subsequences. The author admits that Takefuji et al. outline an elegant way to map the problem onto neural architectures, but suggests that such mappings can be augmented with empirical knowledge (e.g., free energy values of base pairs and substructures) and the ability to learn. In their reply, Y. Takefuji and K.-C. Lee hold that the necessity of the learning capability for the RNA secondary structure prediction is questionable. They believe that the task is to build a robust parallel algorithm considering more thermodynamic properties in the model.<>
  • Keywords
    graph theory; learning systems; neural nets; parallel algorithms; RNA secondary structure prediction; circle graph; learning capability; mappings; near-maximum independent set; neural networks; parallel algorithm; Adaptive filters; Cognition; Distributed processing; Microstructure; Neural networks; Noise measurement; Noise reduction; Noise robustness; Parallel algorithms; RNA;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/72.80347
  • Filename
    80347