Complexity reduction in context-dependent DNA substitution models.
Journal Article (Journal Article)
MOTIVATION: 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.
Full Text
Duke Authors
Cited Authors
- Majoros, WH; Ohler, U
Published Date
- January 15, 2009
Published In
Volume / Issue
- 25 / 2
Start / End Page
- 175 - 182
PubMed ID
- 19017657
Pubmed Central ID
- PMC2732293
Electronic International Standard Serial Number (EISSN)
- 1367-4811
Digital Object Identifier (DOI)
- 10.1093/bioinformatics/btn598
Language
- eng
Conference Location
- England