Finding a Common Motif of RNA Sequences Using Genetic Programming: The GeRNAMo System
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gp-bibliography.bib Revision:1.8028
- @Article{Shahar:2007:tCBB,
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author = "Shahar Michal and Tor Ivry and Omer Schalit-Cohen and
Moshe Sipper and Danny Barash",
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title = "Finding a Common Motif of RNA Sequences Using Genetic
Programming: The GeRNAMo System",
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journal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
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year = "2007",
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volume = "4",
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number = "4",
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pages = "596--610",
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month = oct # "-" # dec,
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keywords = "genetic algorithms, genetic programming, RNA, common
motif, microRNA, STGP",
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DOI = "doi:10.1109/tcbb.2007.1045",
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size = "15 pages",
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abstract = "We focus on finding a consensus motif of a set of
homologous or functionally related RNA molecules.
Recent approaches to this problem have been limited to
simple motifs, require sequence alignment, and make
prior assumptions concerning the data set. We use
genetic programming to predict RNA consensus motifs
based solely on the data set. Our system -- dubbed
GeRNAMo (Genetic programming of RNA Motifs) -- predicts
the most common motifs without sequence alignment and
is capable of dealing with any motif size. Our program
only requires the maximum number of stems in the motif,
and if prior knowledge is available the user can
specify other attributes of the motif (e.g., the range
of the motif's minimum and maximum sizes), thereby
increasing both sensitivity and speed. We describe
several experiments using either ferritin iron response
element (IRE); signal recognition particle (SRP); or
microRNA sequences, showing that the most common motif
is found repeatedly, and that our system offers
substantial advantages over previous methods.",
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notes = "ECJ, STGP, mutation
evolved motif uses special functions h5,h3, ss and
length(min length, max length). Favourable comparison
with GPRM \cite{Yuh-JyhHu:2003:NAR}.",
- }
Genetic Programming entries for
Shahar Michal
Tor Ivry
Omer Schalit-Cohen
Moshe Sipper
Danny Barash
Citations