Challenges rising from learning motif evaluation functions using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Leung-Yau Lo and Tak-Ming Chan and Kin-Hong Lee and 
                 Kwong-Sak Leung",
  title =        "Challenges rising from learning motif evaluation
                 functions using genetic programming",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "171--178",
  keywords =     "genetic algorithms, genetic programming,
                 Bioinformatics, computational, systems and synthetic
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830515",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Motif discovery is an important Bioinformatics problem
                 for deciphering gene regulation. Numerous
                 sequence-based approaches have been proposed employing
                 human specialist motif models (evaluation functions),
                 but performance is so unsatisfactory on benchmarks that
                 the underlying information seems to have already been
                 exploited and have doomed. However, we have found that
                 even a simple modified representation still achieves
                 considerably high performance on a challenging
                 benchmark, implying potential for sequence-based motif
                 discovery. Thus we raise the problem of learning motif
                 evaluation functions. We employ Genetic programming
                 (GP) which has the potential to evolve human
                 competitive models. We take advantage of the terminal
                 set containing specialist-model-like components and
                 have tried three fitness functions. Results exhibit
                 both great challenges and potentials. No models learnt
                 can perform universally well on the challenging
                 benchmark, where one reason may be the data
                 appropriateness for sequence-based motif discovery.
                 However, when applied on different widely-tested
                 datasets, the same models achieve comparable
                 performance to existing approaches based on specialist
                 models. The study calls for further novel GP to learn
                 different levels of effective evaluation models from
                 strict to loose ones on exploiting sequence information
                 for motif discovery, namely quantitative functions,
                 cardinal rankings, and learning feasibility
  notes =        "Also known as \cite{1830515} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",

Genetic Programming entries for "Peter" Leung-Yau Lo Tak-Ming Chan Kin-Hong Lee Kwong-Sak Leung