Abstract
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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Acknowledgment
This research was supported by the National Key Research And Development Program of China (No. 2016YFC0106000), National Natural Science Foundation of China (Grant No. 61302128, 61573166, 61572230, 61671220, 61640218), the Youth Science and Technology Star Program of Jinan City (201406003), the Natural Science Foundation of Shandong Province (ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. ZR2016FB14), the Project of Shandong Province Higher Educational Science and Technology Program, China (Grant No. J16LN07), the Shandong Province Key Research and Development Program, China (Grant No. 2016GGX101022), Research Fund for the Doctoral Program of University of Jinan (No. XBS1604).
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Wang, L., Wang, D., Zhao, Y., Chen, Y. (2017). Prediction of Subcellular Localization of Multi-site Virus Proteins Based on Convolutional Neural Networks. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_53
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