A Genetic Programming Method for the Identification of Signal Peptides and Prediction of Their Cleavage Sites

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@Article{Lennartsson:2004:EURASIP,
  author =       "David Lennartsson and Peter Nordin",
  title =        "A Genetic Programming Method for the Identification of
                 Signal Peptides and Prediction of Their Cleavage
                 Sites",
  journal =      "EURASIP Journal on Advances in Signal Processing",
  year =         "2004",
  volume =       "2004",
  number =       "1",
  pages =        "138--145",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming, demes, parallel sub-populations,
                 signal peptides, bioinformatics, programmatic motif,
                 artificial neural networks, cleavage site",
  ISSN =         "1687-6180",
  publisher =    "Hindawi Publishing Corporation",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.398.1015",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.1015",
  URL =          "http://asp.eurasipjournals.com/content/pdf/1687-6180-2004-153697.pdf",
  DOI =          "doi:doi:10.1155/S1110865704309108",
  size =         "8 pages",
  abstract =     "A novel approach to signal peptide identification is
                 presented. We use an evolutionary algorithm for
                 automatic evolution of classification programs,
                 so-called programmatic motifs. The variant of
                 evolutionary algorithm used is called genetic
                 programming where a population of solution candidates
                 in the form of full computer programs is evolved, based
                 on training examples consisting of signal peptide
                 sequences. The method is compared with a previous work
                 using artificial neural network (ANN) approaches. Some
                 advantages compared to ANNs are noted. The programmatic
                 motif can perform computational tasks beyond that of
                 feed-forward neural networks and has also other
                 advantages such as readability. The best motif evolved
                 was analysed and shown to detect the h-region of the
                 signal peptide. A powerful parallel computer cluster
                 was used for the experiment.",
  notes =        "Saida Medical AB, Stena Center 1A, Goteborg SE-412 92,
                 Sweden",
}

Genetic Programming entries for David Lennartsson Peter Nordin

Citations