Protein Motif Discovery with Linear Genetic Programming

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

@InProceedings{conf/kes/Seehuus05,
  title =        "Protein Motif Discovery with Linear Genetic
                 Programming",
  author =       "Rolv Seehuus",
  year =         "2005",
  booktitle =    "Knowledge-Based Intelligent Information and
                 Engineering Systems: 9th International Conference, KES
                 2005, Proceedings, Part III",
  editor =       "Rajiv Khosla and Robert J. Howlett and 
                 Lakhmi C. Jain",
  volume =       "3683",
  series =       "Lecture Notes in Computer Science",
  pages =        "770--776",
  address =      "Melbourne, Australia",
  publisher_address = "Berlin / Heidelberg",
  month =        sep # " 14-16",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming, ListGP",
  bibdate =      "2005-09-05",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/kes/kes2005-3.html#Seehuus05",
  ISBN =         "3-540-28896-1",
  DOI =          "doi:10.1007/11553939_109",
  size =         "7 pages",
  abstract =     "There have been published some studies of genetic
                 programming as a way to discover motifs in proteins and
                 other biological data. These studies have been small,
                 and often used domain knowledge to improve search. In
                 this paper we present a genetic programming algorithm,
                 that does not use domain knowledge, with results on 44
                 different protein families. We demonstrate that our
                 list-based representation, given a fixed amount of
                 processing resources, is able to discover meaningful
                 motifs with good classification performance. Sometimes
                 comparable to or even surpassing that of motifs found
                 in a database of manually created motifs. We also
                 investigate introduction of gaps in our algorithm, and
                 it seems that this give a small increase in
                 classification accuracy and recall, but with reduced
                 precision.",
  notes =        "hardware search speed up chip, PMC. regular
                 expressions. Max 64 residues, no grammar?, wildcards,
                 flexible gaps",
}

Genetic Programming entries for Rolv Seehuus

Citations