Automated discovery of protein motifs with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "John R. Koza and David Andre",
  title =        "Automated discovery of protein motifs with genetic
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "38--49",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "12 pages",
  abstract =     "Automated methods of machine learning may prove to be
                 useful in discovering biologically meaningful
                 information hidden in the rapidly growing databases of
                 DNA sequences and protein sequences.

                 Genetic programming is an extension of the genetic
                 algorithm in which a population of computer programs is
                 bred, over a series of generations, in order to solve a
                 problem. Genetic programming is capable of evolving
                 complicated problem-solving expressions of unspecified
                 size and shape. Moreover, when automatically defined
                 functions are added to genetic programming, genetic
                 programming becomes capable of efficiently capturing
                 and exploiting recurring sub-patterns. This chapter
                 describes how genetic programming with automatically
                 defined functions successfully evolved motifs for
                 detecting the D-E-A-D box family of proteins and for
                 detecting the manganese superoxide dismutase family.
                 Both motifs were evolved without prespecifying their
                 length. Both evolved motifs employed automatically
                 defined functions to capture the repeated use of common
                 subexpressions. When tested against the SWISS-PROT
                 database of proteins, the two genetically evolved
                 consensus motifs detect the two families either as
                 well, or slightly better than, the comparable
                 human-written motifs found in the PROSITE database.",
  notes =        "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em
                 Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em
                 email} {\em URL:}",

Genetic Programming entries for John Koza David Andre