Protein Motif Discovery with Linear Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{conf/kes/Seehuus05,
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title = "Protein Motif Discovery with Linear Genetic
Programming",
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author = "Rolv Seehuus",
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year = "2005",
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booktitle = "Knowledge-Based Intelligent Information and
Engineering Systems: 9th International Conference, KES
2005, Proceedings, Part III",
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editor = "Rajiv Khosla and Robert J. Howlett and
Lakhmi C. Jain",
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volume = "3683",
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series = "Lecture Notes in Computer Science",
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pages = "770--776",
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address = "Melbourne, Australia",
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publisher_address = "Berlin / Heidelberg",
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month = sep # " 14-16",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, linear
genetic programming, ListGP",
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bibdate = "2005-09-05",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/kes/kes2005-3.html#Seehuus05",
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ISBN = "3-540-28896-1",
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DOI = "doi:10.1007/11553939_109",
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size = "7 pages",
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abstract = "There have been published some studies of genetic
programming as a way to discover motifs in proteins and
other biological data. These studies have been small,
and often used domain knowledge to improve search. In
this paper we present a genetic programming algorithm,
that does not use domain knowledge, with results on 44
different protein families. We demonstrate that our
list-based representation, given a fixed amount of
processing resources, is able to discover meaningful
motifs with good classification performance. Sometimes
comparable to or even surpassing that of motifs found
in a database of manually created motifs. We also
investigate introduction of gaps in our algorithm, and
it seems that this give a small increase in
classification accuracy and recall, but with reduced
precision.",
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notes = "hardware search speed up chip, PMC. regular
expressions. Max 64 residues, no grammar?, wildcards,
flexible gaps",
- }
Genetic Programming entries for
Rolv Seehuus
Citations