Classifying Proteins as Extracellular using Programmatic Motifs and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  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, arithmetic
                 functions, biological structure, cellular location,
                 computerised algorithms, conditional operations, data
                 structures, evolutionary computation technique,
                 extracellular proteins, genetically evolved algorithm,
                 genetically evolved classification algorithm, human
                 created five way algorithm, human intelligence, living
                 organism, macro definitions, newly sequenced proteins,
                 programmatic motif, programmatic motifs, protein
                 classification, protein motif, protein sequences, set
                 creating operations, statistical techniques,
                 subroutines, two way classification algorithm, biology
                 computing, molecular biophysics, pattern
                 classification, proteins",
  ISBN =         "0-7803-4869-9",
  URL =          "",
  DOI =          "doi:10.1109/ICEC.1998.699503",
  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 76percent accuracy.

                 This paper describes a two-way classification algorithm
                 that was evolved using genetic programming with
                 83percent 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
  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