title: Complexity reduction in context-dependent DNA substitution models.
abstract: BACKGROUND
  The modeling of conservation patterns in genomic DNA has become increasingly popular for a number of bioinformatic applications. While several systems developed to date incorporate context-dependence in their substitution models, the impact on computational complexity and generalization ability of the resulting higher order models invites the question of whether simpler approaches to context modeling might permit appreciable reductions in model complexity and computational cost, without sacrificing prediction accuracy.
  
  
  RESULTS
  We formulate several alternative methods for context modeling based on windowed Bayesian networks, and compare their effects on both accuracy and computational complexity for the task of discriminating functionally distinct segments in vertebrate DNA. Our results show that substantial reductions in the complexity of both the model and the associated inference algorithm can be achieved without reducing predictive accuracy.
authors.affiliation: Institute for Genome Sciences & Policy, Duke University, Durham, NC, USA. bmajoros@duke.edu
authors.forename: William H
authors.initials: WH
authors.lastname: Majoros
authors.forename: Uwe
authors.initials: U
authors.lastname: Ohler
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry; D016678:Genome; D008957:Models, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
chemical_list: D004247:DNA