Prediction of Subcellular Localization of Multi-site Virus Proteins Based on Convolutional Neural Networks

Prediction of subcellular localization is critical for the analysis of mechanism and functions of proteins and biological research. A series of efficient methods have been proposed to identify subcellular localization, but challenges still exist. In this paper, a novel feature extraction method, denoted as F-Dipe, is proposed to identify subcellular localization. F-Dipe, which is based on dipeptide pseudo amino acid composition method, improves the performance of multi-site prediction by increasing the focus information of proteins. Besides, convolution neural networks, denoted as CNN, is utilized to predict the subcellular localization of multi-site virus proteins. The multi-label k-nearest neighbor algorithm, denoted as MLKNN, is a base classifier to verify the performance of F-Dipe and CNN. The best overall accuracy of F-Dipe on dataset S from the predictor of MLKNN is 59.92%, higher than the accuracy of pseudo amino acid based features method, denoted as PseAAC, 57.14% and the best overall accuracy of F-Dipe on database S from the predictor of CNN is 62.3%, better than from the predictor of MLKNN 59.92%.

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