Generation of Hidden Markov Model Describing Complex Motif in DNA Sequences

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

@Article{Yada:1999:TIPSJ,
  author =       "Tetsushi Yada and Yasushi Totoki and Kiyoshi Asai and 
                 Masato Ishikawa",
  title =        "Generation of Hidden Markov Model Describing Complex
                 Motif in DNA Sequences",
  journal =      "Transactions of Information Processing Society of
                 Japan",
  year =         "1999",
  volume =       "40",
  number =       "2",
  pages =        "750--767",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0387-5806",
  ISSN =         "1882-7764",
  URL =          "http://id.nii.ac.jp/1001/00012856/",
  size =         "18 pages",
  abstract =     "We have developed a method for the generation of
                 hidden Markov model (HMM) representing complex motif in
                 DNA sequences. The procedures of the method are as
                 follows: (1) design of HMMs for elemental motifs in
                 given DNA sequences; (2) construction of a complex
                 motif HMM consisting of the elemental motif HMMs.
                 Statistical analysis and genetic programming (GP) were
                 applied to the respective procedures. At step (1),
                 left-to-right HMMs were designed and their lengths were
                 determined by a statistical significance. At step (2),
                 probabilistic tree describing HMMs was defined and its
                 structure was optimized by GP against a complex motif.
                 Concatenation, probabilistic union, probabilistic
                 closure, etc. were attached to nonterminal nodes. The
                 elemental motif HMMs and an HMM for any a letter were
                 attached to terminal nodes. In the method, the advance
                 design of elemental motif HMMs and adoption of
                 probabilistic tree as encoding rule of GP lead to
                 efficient generation of complex motif HMM. It was
                 observed that the generated HMM can detect the complex
                 motif in uncharacterized DNA sequences with high
                 accuracy. Further, the HMM is full of interesting
                 suggestions of the complex motif. (author abst.)",
  notes =        "In
                 Japanese.

                 http://sciencelinks.jp/j-east/article/199911/000019991199A0244916.php",
}

Genetic Programming entries for Tetsushi Yada Yasushi Totoki Kiyoshi Asai Masato Ishikawa

Citations