Challenges rising from learning motif evaluation functions using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Lo:2010:gecco,

• 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 biology",
• 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 classifications.",
• 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

Citations