Human microRNA prediction through a probabilistic co-learning model of sequence and structure

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  author =       "Jin-Wu Nam and Ki-Roo Shin and Jinju Han and 
                 Yoontae Lee and V. Narry Kim and Byoung-Tak Zhang",
  title =        "Human microRNA prediction through a probabilistic
                 co-learning model of sequence and structure",
  journal =      "Nucleic Acids Research",
  year =         "2005",
  volume =       "33",
  number =       "11",
  pages =        "3570--3581",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1093/nar/gki668",
  abstract =     "MicroRNAs (miRNAs) are small regulatory RNAs of ~22
                 nt. Although hundreds of miRNAs have been identified
                 through experimental complementary DNA cloning methods
                 and computational efforts, previous approaches could
                 detect only abundantly expressed miRNAs or close
                 homologs of previously identified miRNAs. Here, we
                 introduce a probabilistic co-learning model for miRNA
                 gene finding, ProMiR, which simultaneously considers
                 the structure and sequence of miRNA precursors
                 (pre-miRNAs). On 5-fold cross-validation with 136
                 referenced human datasets, the efficiency of the
                 classification shows 73percent sensitivity and
                 96percent specificity. When applied to genome screening
                 for novel miRNAs on human chromosomes 16, 17, 18 and
                 19, ProMiR effectively searches distantly homologous
                 patterns over diverse pre-miRNAs, detecting at least 23
                 novel miRNA gene candidates. Importantly, the miRNA
                 gene candidates do not demonstrate clear sequence
                 similarity to the known miRNA genes. By quantitative
                 PCR followed by RNA interference against Drosha, we
                 experimentally confirmed that 9 of the 23
                 representative candidate genes express transcripts that
                 are processed by the miRNA biogenesis enzyme Drosha in
                 HeLa cells, indicating that ProMiR may successfully
                 predict miRNA genes with at least 40percent accuracy.
                 Our study suggests that the miRNA gene family may be
                 more abundant than previously anticipated, and confer
                 highly extensive regulatory networks on eukaryotic
  notes =        "PMID: Compares with esRCSG \cite{nam:evows04}",

Genetic Programming entries for Jin-Wu Nam Ki-Roo Shin Jinju Han Yoontae Lee V Narry Kim Byoung-Tak Zhang