We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
CONTRAfold: RNA secondary structure prediction without physics-based models.
- Authors
Do, Chuong B; Woods, Daniel A; Batzoglou, Serafim
- Abstract
For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs) have emerged as an alternative probabilistic methodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic methods have not replaced free energy minimization methods as the tool of choice for secondary structure prediction, as the accuracies of the best current SCFGs have yet to match those of the best physics-based models.
- Publication
Bioinformatics (Oxford, England), 2006, Vol 22, Issue 14, pe90
- ISSN
1367-4811
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/btl246