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Therefore, the traditional deep neural networks designed for image processing cannot be directly applied on sequence sets.
We have created the ProteinNet series of data sets to provide a standardized mechanism for training and assessing data-driven models of protein sequence-structure relationships.
Results: Here, a very deep neural network, the deep inception-inside-inception networks (Deep3I), is proposed for protein secondary structure prediction and a software tool was implemented using this network.
This sequential model achieves 70. 3% Q8 accuracy on CB513 with a single model; an ensemble of these models produces 71. 4% Q8 accuracy on the same test set, improving upon the previous overall state of the art for the eight-class secondary structure problem.
We first train a series of deep neural networks to predict eight-class secondary structure labels given a protein's amino acid sequence information and find that using recent methods for regularization, such as dropout and weight-norm constraining, leads to measurable gains in accuracy.
Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data.