A Co-Evolutionary Framework for Regulatory Motif Discovery

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

@InProceedings{Lones:2007:cec,
  author =       "Michael A. Lones and Andy M. Tyrrell",
  title =        "A Co-Evolutionary Framework for Regulatory Motif
                 Discovery",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  address =      "Singapore",
  month =        "25-28 " # sep,
  pages =        "3894--3901",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1436.pdf",
  DOI =          "doi:10.1109/CEC.2007.4424978",
  size =         "8 pages",
  keywords =     "genetic algorithms, genetic programming, biology
                 computing, evolutionary computation, genetics, pattern
                 classification, Boolean classification rules,
                 co-evolutionary framework, co-expressed genes,
                 regulatory motif discovery, sequence classifiers",
  abstract =     "In previous work, we have shown how an evolutionary
                 algorithm with a clustered population can be used to
                 concurrently discover multiple regulatory motifs
                 present within the promoter sequences of co-expressed
                 genes. In this paper, we extend the algorithm by
                 co-evolving a population of Boolean classification
                 rules in parallel with the motif population. Results
                 using synthetic data suggest that this approach allows
                 poorly conserved motifs to be identified in promoter
                 sequences an order of magnitude longer than using
                 population clustering alone, whilst results using
                 muscle-specific promoter data show the algorithm is
                 able to evolve meaningful sequence classifiers in
                 parallel with motifs' suggesting that co-evolution
                 provides a suitable framework for composite motif
                 discovery within eukaryotic sequences.",
  homepage =     "http://www-users.york.ac.uk/~mal503/",
  notes =        "also known as \cite{4424978}. Page numbers from IEEE
                 Xplore 2009. 3896 GP-like, binary tree.

                 CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Michael A Lones Andrew M Tyrrell

Citations