Robust Machine Learning Algorithms Predict MicroRNA Genes and Targets

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@InCollection{Saetrom200725,
  author =       "Pal Saetrom and Ola {Snove Jr.}",
  title =        "Robust Machine Learning Algorithms Predict MicroRNA
                 Genes and Targets",
  editor =       "John J. Rossi and Gregory J. Hannon",
  booktitle =    "MicroRNA Methods",
  publisher =    "Academic Press",
  year =         "2007",
  volume =       "427",
  pages =        "25--49",
  series =       "Methods in Enzymology",
  ISSN =         "0076-6879",
  DOI =          "DOI:10.1016/S0076-6879(07)27002-8",
  URL =          "http://www.sciencedirect.com/science/article/B7CV2-4PGY39X-2/2/b5dbc8edf791a8a25436462482a18d53",
  keywords =     "genetic algorithms, genetic programming",
  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