Predicting protein structural classes with autoencoder neural networks

Autoencoder neural networks was firstly introduced by G.E.Hinton to reduce the dimensionality of data.In this paper, we propose a new way to classify protein structural classes using autoencoder neural networks. The optimum configurations with respect to the size of hidden layers are identified. The problem of training a deep autoencoder for classifying protein structural classes is addressed. Stacked autoencoder is used for reducing the convergence time of training. We design a series of experiments to testify the effectiveness of using autoencoder networks to tackle the problem of predicting protein structure. The experimental results show that our proposed method is competitive with state-of-the-art SVM methods in predicting protein structure class.