Multiple Sequence Alignment with Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Conrad Shyu and Luke Sheneman and James A. Foster",
  title =        "Multiple Sequence Alignment with Evolutionary
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2004",
  volume =       "5",
  number =       "2",
  pages =        "121--144",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, multiple
                 sequence alignment, genetic algorithm, progressive
                 alignments, DNA sequences",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1023/B:GENP.0000023684.05565.78",
  abstract =     "we provide a brief review of current work in the area
                 of multiple sequence alignment (MSA) for DNA and
                 protein sequences using evolutionary computation (EC).
                 We detail the strengths and weaknesses of EC techniques
                 for MSA. In addition, we present two novel approaches
                 for inferring MSA using genetic algorithms. Our first
                 approach uses a GA to evolve an optimal guide tree in a
                 progressive alignment algorithm and serves as an
                 alternative to the more traditional heuristic
                 techniques such as neighbor-joining. The second novel
                 approach facilitates the optimization of a consensus
                 sequence with a GA using a vertically scalable encoding
                 scheme in which the number of iterations needed to find
                 the optimal solution is approximately the same
                 regardless the number of sequences being aligned. We
                 compare both of our novel approaches to the popular
                 progressive alignment program Clustal W. Experiments
                 have confirmed that EC constitutes an attractive and
                 promising alternative to traditional heuristic
                 algorithms for MSA.",
  notes =        "Initiatives for Bioinformatics and Evolutionary
                 Studies (IBEST), Department of Bioinformatics and
                 Computational Biology, University of Idaho, Moscow,
                 Idaho 83844-1010, USA

                 Part of \cite{banzhaf:2004:biogec} Special Issue on
                 Biological Applications of Genetic and Evolutionary
                 Computation Guest Editor(s): Wolfgang Banzhaf , James

Genetic Programming entries for Conrad Shyu Luke Sheneman James A Foster