Classifying Proteins as Extracellular using Programmatic Motifs and Genetic Programming   [GP]

by

Koza, J., R., Bennett III, F., H. and Andre, D.

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Info: Proceedings of the 1998 IEEE World Congress on Computational Intelligence (Conference proceedings), 1998, p. 212-217
Keywords:genetic algorithms, genetic programming
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 [GP] 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, [DS] 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 [EC] 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
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BibTex:
@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",
  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",
}