Structural analysis of regulatory DNA sequences using grammar inference and Support Vector Machine
Created by W.Langdon from
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- @Article{Damasevicius2010633,
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author = "Robertas Damasevicius",
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title = "Structural analysis of regulatory DNA sequences using
grammar inference and Support Vector Machine",
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journal = "Neurocomputing",
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volume = "73",
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number = "4-6",
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pages = "633--638",
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year = "2010",
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note = "Bayesian Networks / Design and Application of Neural
Networks and Intelligent Learning Systems (KES 2008 /
Bio-inspired Computing: Theories and Applications
(BIC-TA 2007)",
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ISSN = "0925-2312",
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DOI = "doi:10.1016/j.neucom.2009.09.018",
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URL = "http://www.sciencedirect.com/science/article/B6V10-4XRYT4P-1/2/2e5b008bc8df4d5a39553b40fe6728c3",
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keywords = "genetic algorithms, genetic programming, DNA sequence
analysis, Grammar inference, L-grammar, Support Vector
Machine, SVM",
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abstract = "Regulatory DNA sequences such as promoters or splicing
sites control gene expression and are important for
successful gene prediction. Such sequences can be
recognized by certain patterns or motifs that are
conserved within a species. These patterns have many
exceptions which makes the structural analysis of
regulatory sequences a complex problem. Grammar rules
can be used for describing the structure of regulatory
sequences; however, the manual derivation of such rules
is not trivial. In this paper, stochastic L-grammar
rules are derived automatically from positive examples
and counterexamples of regulatory sequences using
genetic programming techniques. The fitness of grammar
rules is evaluated using a Support Vector Machine (SVM)
classifier. SVM is trained on known sequences to obtain
a discriminating function which serves for evaluating a
candidate grammar ruleset by determining the percentage
of generated sequences that are classified correctly.
The combination of SVM and grammar rule inference can
mitigate the lack of structural insight in machine
learning approaches such as SVM.",
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notes = "TATA box",
- }
Genetic Programming entries for
Robertas Damasevicius
Citations