Classifying Proteins as Extracellular using Programmatic Motifs and Genetic Programming

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

@InProceedings{koza:1998:cpeupmGP,
  author =       "John R. Koza and Forrest H {Bennett III} and 
                 David Andre",
  title =        "Classifying Proteins as Extracellular using
                 Programmatic Motifs and Genetic Programming",
  booktitle =    "Proceedings of the 1998 IEEE World Congress on
                 Computational Intelligence",
  year =         "1998",
  pages =        "212--217",
  address =      "Anchorage, Alaska, USA",
  month =        "5-9 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-4869-9",
  URL =          "http://www.genetic-programming.com/jkpdf/icec1998.pdf",
  file =         "c037.pdf",
  size =         "6 pages",
  abstract =     "As newly sequenced proteins are deposited into the
                 world' s ever-growing archive of protein sequences,
                 they are typically immediately tested by various
                 computerized algorithms for clues as to their
                 biological structure and function. One question about a
                 new protein involves its cellular location - that is,
                 where the protein resides in a living organism
                 (extracellular, intracellular, etc.). A 1997 paper
                 reported a human-created five-way algorithm for
                 cellular location created using statistical techniques
                 with 76% accuracy.

                 This paper describes a two-way classification algorithm
                 that was evolved using genetic programming with 83%
                 accuracy for determining whether a protein is
                 extracellular. 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. The genetically evolved program
                 constitutes an instance of an evolutionary computation
                 technique producing a solution to a problem that is
                 competitive with that produced using human
                 intelligence.",
  notes =        "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
                 World Congress on Computational Intelligence",
}

Genetic Programming entries for John Koza Forrest Bennett David Andre