Using Programmatic Motifs and Genetic Programming to Classify Protein Sequences as to Extracellular and Membrane Cellular Location

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{koza:1998:pmGPcpsemcl,
  author =       "John Koza and Forrest Bennett and David Andre",
  title =        "Using Programmatic Motifs and Genetic Programming to
                 Classify Protein Sequences as to Extracellular and
                 Membrane Cellular Location",
  booktitle =    "Evolutionary Programming VII: Proceedings of the
                 Seventh Annual Conference on Evolutionary Programming",
  year =         "1998",
  editor =       "V. William Porto and N. Saravanan and D. Waagen and 
                 A. E. Eiben",
  volume =       "1447",
  series =       "LNCS",
  address =      "Mission Valley Marriott, San Diego, California, USA",
  publisher_address = "Berlin",
  month =        "25-27 " # mar,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64891-7",
  URL =          "http://www.genetic-programming.com/jkpdf/ep1998.pdf",
  doi =          "doi:10.1007/BFb0040753",
  abstract =     "As newly sequenced proteins are deposited into the
                 world's ever-growing archive of protein sequences, they
                 are typically immediately tested by various algorithms
                 for clues as to their biological structure and
                 function. One question about a new protein involves its
                 cellular location ­p; that is, where the protein
                 resides in a living organism (extracellular, membrane,
                 etc.). A human-created five-way algorithm for cellular
                 location using statistical techniques with 76% accuracy
                 was recently reported. This paper describes a two-way
                 algorithm that was evolved using genetic programming
                 with 83% accuracy for determining whether a protein is
                 extracellular and with 89% accuracy for membrane
                 proteins. Unlike the statistical calculation, the
                 genetically evolved algorithm employs a large and
                 varied arsenal of computational capabilities, including
                 arithmetic functions, conditional operations,
                 subroutines, iterations, memory, data structures,
                 set-creating operations, macro definitions, recursion,
                 etc. The genetically evolved classification algorithm
                 can be viewed as an extension (which we call a
                 programmatic motif) of the conventional notion of a
                 protein motif.",
  notes =        "EP-98.

                 ",
}

Genetic Programming entries for John Koza Forrest Bennett David Andre