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A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model
[article]
2017
bioRxiv
pre-print
Motivation: A popular approach for predicting RNA secondary structure is the thermodynamic nearest neighbor model that finds a thermodynamically most stable secondary structure with the minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a
doi:10.1101/205047
fatcat:bqwnbtvskjcw5fds43gvcnidrm