Complexity reduction in context-dependent DNA substitution models.

ABSTRACT:

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 verte

SUBMITTER: Majoros WH 

PROVIDER: S-EPMC2732293 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature