Rich Parameterization Improves RNA Structure Prediction

Motivation. Current approaches to RNA structure prediction range from physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, to machinelearning (ML) techniques. While the methods for parameter estimation are successfully shifting toward ML-based approaches, the model parameterizations so far remained fairly constant and all models to date have relatively few parameters. We propose a move to much richer parameterizations. Contribution. We study the potential contribution of increasing the amount of information utilized by folding prediction models to the improvement of their prediction quality. This is achieved by proposing novel models, which refine previous ones by examining more types of structural elements, and larger sequential contexts for these elements. We argue that with suitable learning techniques, not being tied to features whose weights could be determined experimentally, and having a large enough set of examples, one could de�...

• Type: Journal
• Year: 2011
• Where: RECOMB
• Authors: Shay Zakov, Yoav Goldberg, Michael Elhadad, Michal Ziv-Ukelson
• Keywords: Computational Biology, Feature Representations, Ml Techniques, RECOMB 2011, Rna Structure Prediction