Syntactic Pattern Recognition Using Finite Inductive Strings

  • Paul Fisher
  • Howard Fisher
  • Jinsuk Baek
  • Cleopas Angaye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5780)


A syntactic pattern recognition technique is described based upon a mathematical principle associated with finite sequences of symbols. The technique allows for fast recognition of patterns within strings, including the ability to recognize expected symbols that are close to the desired symbols, and mutations as well as both local and global substring matching. This allowance of deviation permits sequences to be subject to error and still be recognized. Some examples are provided illustrating the technique.


Pattern recognition finite inductive sequences syntactic pattern recognition genome recognition 


  1. 1.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology 215(3), 403–410 (1990)CrossRefPubMedGoogle Scholar
  2. 2.
    Buckingham, S.D.: Scientific software: seeing the SNP’s between us. Nature Methods 5, 903–908 (2008)CrossRefGoogle Scholar
  3. 3.
    Case, J., Fisher, P.S.: Long Term Memory Modules. Bulletin of Mathematical Biology 46(2) (1984)Google Scholar
  4. 4.
    Das, S., Fisher, P.S., Zhang, H.: Efficient Parallel Algorithms for Pattern Recognition. In: Proceedings of Twenty-sixth Hawaii International Conference in Systems Sciences (January 1993)Google Scholar
  5. 5.
    Pearson, W.: Flexible sequence similarity searching with FASTA3 program package. In: Misener, S., Krawety, S.A. (eds.) Bioinformatics Methods and Protocols, pp. 185–219. Humana Press, Inc., Totowa (2000)Google Scholar
  6. 6.
    Pearson, W.R., Lipman, D.J.: Improved tools for biological sequence comparison. Proc. National Academy of Science 85, 2444–2448 (1988)CrossRefGoogle Scholar
  7. 7.
    Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. Journal of Molecular Biology 147, 195–197 (1981)CrossRefPubMedGoogle Scholar
  8. 8.
    Uberbacher, E.: Computing the Genome,
  9. 9.
    Wang, G.Y., Fisher, P.: Knowledge Acquisition: Neural Network Learning. In: Data Mining and Knowledge Discovery: Theory, Tools, and Technology. II SPIE, vol. 4057, pp. 117–128 (2000)Google Scholar
  10. 10.
    Xu, Y., Mural, R.J., Einstein, J.R., Shah, M., Uberbacher, E.C.: GRAIL: A Multi-Agent Neural Network System for Gene Identification. Proceedings of The IEEE 84(10), 1544–1552 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Paul Fisher
    • 1
  • Howard Fisher
    • 2
  • Jinsuk Baek
    • 1
  • Cleopas Angaye
    • 3
  1. 1.Department of Computer ScienceWinston-Salem State UniversityNorth CarolinaUnited States
  2. 2.Fisher CompanySalt Lake CityUnited States
  3. 3.National Information Technology Development AgencyNigeria

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