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http://dx.doi.org/10.6109/jkiice.2018.22.4.728

Architectures of Convolutional Neural Networks for the Prediction of Protein Secondary Structures  

Chi, Sang-Mun (Department of Computer Science, Kyungsung University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.5, 2018 , pp. 728-733 More about this Journal
Abstract
Deep learning has been actively studied for predicting protein secondary structure based only on the sequence information of the amino acids constituting the protein. In this paper, we compared the performances of the convolutional neural networks of various structures to predict the protein secondary structure. To investigate the optimal depth of the layer of neural network for the prediction of protein secondary structure, the performance according to the number of layers was investigated. We also applied the structure of GoogLeNet and ResNet which constitute building blocks of many image classification methods. These methods extract various features from input data, and smooth the gradient transmission in the learning process even using the deep layer. These architectures of convolutional neural networks were modified to suit the characteristics of protein data to improve performance.
Keywords
Protein secondary structure; Deep learning; Convolutional neural networks; GoogLeNet; ResNet;
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