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:1.7954
- @InProceedings{koza:1998:pmGPcpsemcl,
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author = "John Koza and Forrest Bennett and David Andre",
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title = "Using Programmatic Motifs and Genetic Programming to
Classify Protein Sequences as to Extracellular and
Membrane Cellular Location",
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booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
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year = "1998",
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editor = "V. William Porto and N. Saravanan and D. Waagen and
A. E. Eiben",
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volume = "1447",
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series = "LNCS",
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address = "Mission Valley Marriott, San Diego, California, USA",
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publisher_address = "Berlin",
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month = "25-27 " # mar,
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-64891-7",
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URL = "http://www.genetic-programming.com/jkpdf/ep1998.pdf",
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DOI = "doi:10.1007/BFb0040753",
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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.",
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notes = "EP-98.
",
- }
Genetic Programming entries for
John Koza
Forrest Bennett
David Andre
Citations