The Evaluation of a Stochastic Regular Motif Language for Protein Sequences

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{ross:2001:gecco,
  title =        "The Evaluation of a Stochastic Regular Motif Language
                 for Protein Sequences",
  author =       "Brian J. Ross",
  pages =        "120--128",
  year =         "2001",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2001)",
  editor =       "Lee Spector and Erik D. Goodman and Annie Wu and 
                 W. B. Langdon and Hans-Michael Voigt and Mitsuo Gen and 
                 Sandip Sen and Marco Dorigo and Shahram Pezeshk and 
                 Max H. Garzon and Edmund Burke",
  address =      "San Francisco, California, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "7-11 " # jul,
  keywords =     "genetic algorithms, genetic programming, motif,
                 stochastic regular expressions, grammatical genetic",
  ISBN =         "1-55860-774-9",
  URL =          "http://www.cosc.brocku.ca/~bross/research/gp002.pdf",
  URL =          "http://citeseer.ist.psu.edu/503937.html",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
  abstract =     "A probabilistic regular motif language for protein
                 sequences is evaluated. SRE-DNA is a stochastic regular
                 expression language that combines characteristics of
                 regular expressions and stochastic representations such
                 as Hidden Markov Models. To evaluate its expressive
                 merits, genetic programming is used to evolve SRE-DNA
                 motifs for aligned sets of protein sequences. Different
                 constrained grammatical forms of SRE-DNA expressions
                 are applied to aligned protein sequences from the
                 PROSITE database. Some sequences patterns were
                 precisely determined, while others resulted in good
                 solutions having considerably different features from
                 the PROSITE equivalents. This research establishes the
                 viability of SRE-DNA as a new representation language
                 for protein sequence identification. The practicality
                 of using grammatical genetic programming in stochastic
                 biosequence expression classification is also
                 demonstrated.",
  notes =        "GECCO-2001 A joint meeting of the tenth International
                 Conference on Genetic Algorithms (ICGA-2001) and the
                 sixth Annual Genetic Programming Conference (GP-2001)
                 Part of \cite{spector:2001:GECCO}

                 DCTG-GP Kleene closure=()* 4 grammars tried. Initial
                 pop 2000 culled to 1000. bloat. 'full version of
                 SRE-DNA without guards or nested iteration
                 constraints...was very inefficient' Sicstus Prolog
                 3.8.5.",
}

Genetic Programming entries for Brian J Ross

Citations