Derivation of context-free stochastic L-Grammar rules for promoter sequence modeling using Support Vector Machine
Created by W.Langdon from
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- @InProceedings{Damasevicius:ITA:2008,
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author = "Robertas Damasevicius",
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title = "Derivation of context-free stochastic {L}-Grammar
rules for promoter sequence modeling using Support
Vector Machine",
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booktitle = "XI-th Joint International Scientific Events on
Informatics, Book 2, Advanced Research in Artificial
Intelligence",
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year = "2008",
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editor = "K. Markov and {K. Ivanova} and I. Mitov",
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series = "Information Science and Computing",
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pages = "98--104",
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address = "Varna, Bulgaria",
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publisher_address = "Sofia, Bulgaria",
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month = "23 " # jun # " - 03 " # jul,
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publisher = "Ithea",
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keywords = "genetic algorithms, genetic programming, pattern
recognition, J, 3 life and medical sciences",
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annote = "The Pennsylvania State University CiteSeerX Archives",
-
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
-
language = "en",
-
oai = "oai:CiteSeerX.psu:10.1.1.386.8512",
-
rights = "Metadata may be used without restrictions as long as
the oai identifier remains attached to it.",
-
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.386.8512",
-
URL = "http://www.foibg.com/ibs_isc/ibs-02/ibs-02.htm",
-
URL = "http://www.foibg.com/ibs_isc/ibs-02/IBS-02-p13.pdf",
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size = "7 pages",
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abstract = "Formal grammars can used for describing complex
repeatable structures such as DNA sequences. In this
paper, we describe the structural composition of DNA
sequences using a context-free stochastic L-grammar.
L-grammars are a special class of parallel grammars
that can model the growth of living organisms, e.g.
plant development, and model the morphology of a
variety of organisms. We believe that parallel grammars
also can be used for modelling genetic mechanisms and
sequences such as promoters. Promoters are short
regulatory DNA sequences located upstream of a gene.
Detection of promoters in DNA sequences is important
for successful gene prediction. Promoters can be
recognised by certain patterns that are conserved
within a species, but there are many exceptions which
makes the promoter recognition a complex problem. We
replace the problem of promoter recognition by
induction of context-free stochastic L-grammar rules,
which are later used for the structural analysis of
promoter sequences. L-grammar rules are derived
automatically from the drosophila and vertebrate
promoter datasets using a genetic programming technique
and their fitness is evaluated using a Support Vector
Machine (SVM) classifier. The artificial promoter
sequences generated using the derived L-grammar rules
are analysed and compared with natural promoter
sequences.",
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notes = "http://www.foibg.com/conf/itaf2008.htm",
- }
Genetic Programming entries for
Robertas Damasevicius
Citations