title: Modeling splicing sites with pairwise correlations.
abstract: BACKGROUND
  A new method for finding subtle patterns in sequences is introduced. It approximates the multiple correlations among residuals with pair-wise correlations, with the learning cost O(m(2)n) where n is the number of training sequences, each of length m. The method suits to model splicing sites in human DNA, which are reported to have higher-order dependencies.
  
  
  RESULTS
  By computational experiments, the prediction accuracy of our model was shown to surpass that of previously reported Markov models for the prediction of acceptor sites in human.
  
  
  BACKGROUND
  The C++ source code is available on request from the authors.
authors.affiliation: Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. m-arita@aist.go.jp
authors.forename: Masanori
authors.initials: M
authors.lastname: Arita
authors.forename: Koji
authors.initials: K
authors.lastname: Tsuda
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Asai
mesh_terms: D000465:Algorithms; D020029:Base Pairing / Q000235:genetics; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D022821:RNA Splice Sites / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012689:Sequence Homology, Nucleic Acid; D012984:Software; D013223:Statistics as Topic
chemical_list: D022821:RNA Splice Sites