MotifGP: DNA Motif Discovery Using Multiobjective Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4472

@MastersThesis{Belmadani2016-bn,
  title =        "{MotifGP}: {DNA} Motif Discovery Using Multiobjective
                 Evolution",
  author =       "Manuel Belmadani",
  school =       "School of Electrical Engineering and Computer Science,
                 University of Ottawa",
  year =         "2016",
  type =         "Master degree in Computer Science Specialization in
                 Bioinformatics",
  address =      "Canada",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://hdl.handle.net/10393/34213",
  URL =          "https://ruor.uottawa.ca/bitstream/10393/34213/1/Belmadani_Manuel_2016_thesis.pdf",
  DOI =          "doi:10.20381/ruor-5077",
  size =         "119 pages",
  abstract =     "The motif discovery problem is becoming increasingly
                 important for molecular biologists as new sequencing
                 technologies are producing large amounts of data, at
                 rates which are unprecedented. The solution space for
                 DNA motifs is too large to search with naive methods,
                 meaning there is a need for fast and accurate motif
                 detection tools. We propose MotifGP, a multiobjective
                 motif discovery tool evolving regular expressions that
                 characterize overrepresented motifs in a given input
                 dataset. This thesis describes and evaluates a
                 multiobjective strongly typed genetic programming
                 algorithm for the discovery of network expressions in
                 DNA sequences. Using 13 realistic data sets, we compare
                 the results of our tool, MotifGP, to that of DREME, a
                 state-of-art program. MotifGP outperforms DREME when
                 the motifs to be sought are long, and the specificity
                 is distributed over the length of the motif. For
                 shorter motifs, the performance of MotifGP compares
                 favourably with the state-of-the-art method. Finally,
                 we discuss the advantages of multi-objective
                 optimization in the context of this specific motif
                 discovery problem.",
  notes =        "supervisor Dr. Marcel Turcotte",
}

Genetic Programming entries for Manuel Belmadani

Citations