Advertisement

Promoter Prediction with a GP-Automaton

  • Daniel Howard
  • Karl Benson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

A GP-automaton evolves motif sequences for its states; it moves the point of motif application at transition time using an integer that is stored and evolved in the transition; and it combines motif matches via logical functions that it also stores and evolves in each transition. This scheme learns to predict promoters in human genome. The experiments reported use 5-fold cross validation.

Keywords

Finite State Automaton Promoter Prediction Halt State Bacterial Promoter Finite State Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [Ashlock, 1997]
    Ashlock, D. (1997). GP-Automata for dividing the dollar. In Koza et. al. (Eds) Genetic Programming: Proceedings of the Second Annual Conference, 18–26, Stanford University.Google Scholar
  2. [Benson, 2000a]
    Benson, K. A. (2000). Evolving Finite State Machines with Embedded Genetic Programming for Automatic Target Detection within SAR Imagery In Proceedings of the Congress on Evolutionary Computation, 1543–1549, La Jolla, San Diego, USA.Google Scholar
  3. [Benson, 2000b]
    Benson, K. A. (2000). Performing Automatic Target Detection with evolvable finite state automata. In Journal of Image and Vision Computing, Volume 20, Issue 9–10, Elsevier.Google Scholar
  4. [Cattaneo et. al., 2002]
    Cattaneo E., Rigamonti D., Zuccato C. The Enigma of Huntington’s Disease. Scientific American, December 2002.Google Scholar
  5. [Hannenhalli and Levy, 2001]
    Hannenhalli S. and Levy S. (2001). Promoter prediction in the human genome. In Proceedings of the 9th International Conference on Intelligent Systems for Molecular Biology. Copenhagen, Denmark, July 21-25, 2001, Bioinformatics, Vol 17, Supplement 1, pp. S90–S96. ISSN: 1367-4803.Google Scholar
  6. [Koza, 1999]
    John R. Koza (1999). Genetic Programming III: Darwinian Invention and Problem Solving, MIT Press.Google Scholar
  7. [Lewin, 2000]
    Benjamin Lewin (2000). Genes VII, Oxford University Press.Google Scholar
  8. [Orphanides et al., 1996]
    Orphanides, G., Lagrange, T., Reinberg, D.. The general transcription factors of RNA polymerase II. Genes. Dev., vol. 10, 2657–2683, 1996.CrossRefGoogle Scholar
  9. [Pedersen et al., 1999]
    Pedersen, A. G., Baldi, P., Chauvin, Y., Brunak, S. The biology of eukaryotic promoter prediction-a review. Computers and Chemistry, vol. 23, 191–207, 1999.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Daniel Howard
    • 1
  • Karl Benson
    • 1
  1. 1.Software Evolution Centre, Knowledge and Information Systems Division, QinetiQ LtdMalvern Technology CentreMalvernUK

Personalised recommendations