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Current deep learning methods unable to predict RNA secondary structures

Machine learning of RNA structure is more challenging than you might think. Using synthetic data from ViennaRNA's RNAfold to study the capabilities and shortcomings of neural networks for RNA secondary structure prediction in a controlled setting, we argue that shortcomings in the artificial setting will translate to real data

Posted by Michael T. Wolfinger on in publications. updated Tags: ViennaRNA, AI.