Challenges rising from learning motif evaluation functions using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{Lo:2010:gecco,
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author = "Leung-Yau Lo and Tak-Ming Chan and Kin-Hong Lee and
Kwong-Sak Leung",
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title = "Challenges rising from learning motif evaluation
functions using genetic programming",
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booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
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year = "2010",
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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",
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isbn13 = "978-1-4503-0072-8",
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pages = "171--178",
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keywords = "genetic algorithms, genetic programming,
Bioinformatics, computational, systems and synthetic
biology",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Portland, Oregon, USA",
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DOI = "doi:10.1145/1830483.1830515",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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