Discovering biological motifs with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Rolv Seehuus and Amund Tveit and Ole Edsberg",
  title =        "Discovering biological motifs with genetic
  booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
                 Genetic and evolutionary computation",
  year =         "2005",
  editor =       "Hans-Georg Beyer and Una-May O'Reilly and 
                 Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and 
                 Eric W. Bonabeau and Erick Cantu-Paz and 
                 Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and 
                 Edwin D. {de Jong} and Hod Lipson and Xavier Llora and 
                 Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and 
                 Terence Soule and Andy M. Tyrrell and 
                 Jean-Paul Watson and Eckart Zitzler",
  volume =       "1",
  ISBN =         "1-59593-010-8",
  pages =        "401--408",
  address =      "Washington DC, USA",
  URL =          "",
  DOI =          "doi:10.1145/1068009.1068074",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "25-29 " # jun,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Biological
                 Applications, performance, protein motifs,
  size =         "8 pages",
  abstract =     "Choosing the right representation for a problem is
                 important. In this article we introduce a linear
                 genetic programming approach for motif discovery in
                 protein families, and we also present a thorough
                 comparison between our approach and Koza-style genetic
                 programming using ADFs.

                 In a study of 45 protein families, we demonstrate that
                 our algorithm, given equal processing resources and no
                 prior knowledge in shaping of datasets, consistently
                 generates motifs that are of significantly better
                 quality than those we found by using trees as
                 representation. For several of the studied protein
                 families we evolve motifs comparable to those found in
                 Prosite, a manually curated database of protein

                 Our linear genome gave better results than Koza-style
                 genetic programming for 37 of 45 families. The
                 difference is statistically significant for 24 of the
                 families at the 99 percent confidence level.",
  notes =        "GECCO-2005 A joint meeting of the fourteenth
                 international conference on genetic algorithms
                 (ICGA-2005) and the tenth annual genetic programming
                 conference (GP-2005).

                 ACM Order Number 910052",

Genetic Programming entries for Rolv Seehuus Amund Tveit Ole Edsberg