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RECOMB
2011
Springer

Rich Parameterization Improves RNA Structure Prediction

7 years 9 months ago
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 reļ¬ne 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 deļ...
Shay Zakov, Yoav Goldberg, Michael Elhadad, Michal
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where RECOMB
Authors Shay Zakov, Yoav Goldberg, Michael Elhadad, Michal Ziv-Ukelson
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