Protein secondary structure prediction.

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Generating functional protein variants with variational autoencoders (2020, background): “61 While there have recently been several successes in applying deep learning techniques to mod62 elling protein sequences in tasks including contact prediction [19], secondary structure prediction 63 [20, 21], and prediction of the fitness effects of mutations [22], the possibility of applying gene”
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, background): “In fact, when assessed on different validation sets with less than 25% sequence identity to any protein used in the training, its accuracy was consistently at pair or better than that of other state-of-the-art tools (3,4,15,19,20).”
Supervised Sequence Labelling with Recurrent Neural Networks (2012, background): “Bidirectional recurrent neural networks (BRNNs; Schuster and Paliwal, 1997; Schuster, 1999; Baldi et al., 1999) offer a more elegant solution.”
Critical assessment of high-throughput standalone methods for secondary structure prediction (2011, methodology): “The selected predictors include most of the key SS prediction methods which were identified by Rost [13], such as PHD, PSIPRED, SSpro, PORTER, SABLE and YASPIN and most of the methods recently reviewed by Pirovano and Heringa [20], including PHD, PSIPRED, SSpro, PORTER and YASPIN.”