CONTRAfold is a novel secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring. By incorporating most of the features found in typical thermodynamic models, CONTRAfold achieves the highest single sequence prediction accuracies to date, outperforming currently available probabilistic and physics-based techniques. Our result thus closes the gap between probabilistic and thermodynamic models, demonstrating that statistical learning procedures provide an effective alternative to empirical measurement of thermodynamic parameters for RNA secondary structure prediction.
Chuong Do (firstname.lastname@example.org)
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Do, C.B., Woods,D.A., and Batzoglou, S. (2006)
CONTRAfold: RNA Secondary Structure Prediction without Energy-Based Models.
Bioinformatics, 22(14): e90-e98.