A simple and fast secondary structure prediction method using hidden neural networks

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Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields (2015, methodology): “ has only a hyper-parameter, i.e., the regularization factor, which is used to avoid overfitting. Once it is fixed, we can estimate all the model parameters by solving the optimization problem in Eq. (10). To choose the right regularization factor and examine the stability of our DeepCNF model, we c”
The effect of long‐range interactions on the secondary structure formation of proteins (2005, methodology): “2000), hidden Markov models (Karplus et al. 1998; Lin et al. 2005), and support vector machines (Hua and Sun 2001; Ward et al.”
PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information (2005, methodology): “As a result, many studies have shown that its use to guide sequence alignment improves alignment quality, especially between distant sequences (6–8 , 11– 15 The secondary structure integration options of PRALINE involve the use of any one of the seven prediction methods that are listed [PHDpsi (27),”
Prediction of protein structure from ideal forms (2008, background): “15 This degree of variation was sufficient that usually at least one of the variants was a good approximation to the true secondary structure.”