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MOTIVATION 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(More)
Empirical discovery of RNA secondary structure is expensive and time consuming, but is a necessary part of exploring function. Software tools exist for performing these predictions, the best of which either heuristic physics modeling or generative learning models. Currently, the best of each are approximately equal in performance. While perfect predictions(More)