Robust Machine Learning Algorithms Predict MicroRNA Genes and Targets
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Saetrom200725,
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author = "Pal Saetrom and Ola {Snove Jr.}",
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title = "Robust Machine Learning Algorithms Predict MicroRNA
Genes and Targets",
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editor = "John J. Rossi and Gregory J. Hannon",
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booktitle = "MicroRNA Methods",
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publisher = "Academic Press",
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year = "2007",
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volume = "427",
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pages = "25--49",
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series = "Methods in Enzymology",
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ISSN = "0076-6879",
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DOI = "DOI:10.1016/S0076-6879(07)27002-8",
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URL = "http://www.sciencedirect.com/science/article/B7CV2-4PGY39X-2/2/b5dbc8edf791a8a25436462482a18d53",
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keywords = "genetic algorithms, genetic programming",
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abstract = "MicroRNAs (miRNA) are nonprotein coding RNAs with the
potential to regulate the gene expression of thousands
of protein coding genes. Current estimates suggest the
number of miRNA genes may be twice of what is currently
known, and the mechanisms governing miRNA targeting
remain elusive. Machine learning algorithms can be used
to create classifiers that capture the characteristics
of verified examples to determine whether genomic
hairpins are similar to verified miRNA genes or if
message 3'UTRs possess known target characteristics.
Algorithms can never replace biological verifications,
but should always be used to guide experimental design.
This chapter focuses on potential problems that must be
addressed when machine learning is used and follows a
practical approach to demonstrate how support vector
machines and genetic programming can predict miRNA
genes and targets.",
- }
Genetic Programming entries for
Pal Saetrom
Ola Snove Jr
Citations