MotifGP: DNA Motif Discovery Using Multiobjective Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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