Prediction of consensus structural motifs in a family of coregulated RNA sequences

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Yuh-JyhHu:2002:NAR,
  author =       "Yuh-Jyh Hu",
  title =        "Prediction of consensus structural motifs in a family
                 of coregulated RNA sequences",
  journal =      "Nucleic Acids Research",
  year =         "2002",
  volume =       "30",
  number =       "17",
  pages =        "3886--3893",
  keywords =     "genetic algorithms, genetic programming",
  broken =       "http://www.ingentaconnect.com/content/oup/nar/2002/00000030/00000017/art03886",
  URL =          "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=137409.pdf",
  URL =          "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=137409",
  DOI =          "doi:10.1093/nar/gkg521",
  size =         "8 pages",
  abstract =     "Given a set of homologous or functionally related RNA
                 sequences, the consensus motifs may represent the
                 binding sites of RNA regulatory proteins. Unlike DNA
                 motifs, RNA motifs are more conserved in structures
                 than in sequences. Knowing the structural motifs can
                 help us gain a deeper insight of the regulation
                 activities. There have been various studies of RNA
                 secondary structure prediction, but most of them are
                 not focused on finding motifs from sets of functionally
                 related sequences. Although recent research shows some
                 new approaches to RNA motif finding, they are limited
                 to finding relatively simple structures, e.g.
                 stemloops. In this paper, we propose a novel genetic
                 programming approach to RNA secondary structure
                 prediction. It is capable of finding more complex
                 structures than stem-loops. To demonstrate the
                 performance of our new approach as well as to keep the
                 consistency of our comparative study, we first tested
                 it on the same data sets previously used to verify the
                 current prediction systems. To show the flexibility of
                 our new approach, we also tested it on a data set that
                 contains pseudo knot motifs which most current systems
                 cannot identify. A web-based user interface of the
                 prediction system is set up at http://bioinfo.
                 cis.nctu.edu.tw/service/gprm/.",
  notes =        "PMID: 12202774

                 p3887 negative examples randomly generated.
                 fitness=F-score. pop=1000, 50gens. Tournament=2 (pop
                 culled to 50percent???). virus 3'-UTR. Matthews
                 correlation coefficient. GP fairly insensitive to
                 crossover and mutation rates. GPRM",
}

Genetic Programming entries for Yuh-Jyh Hu

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