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A viral protein identifying framework based on temporal convolutional network

1 Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian 116622, China
2 School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian, 116024, China
3 School of Computer Science, Dalian University of Technology, Dalian 116024, China

Special Issues: Health Information Processing

The interaction between viral proteins and small molecule compounds is the basis of drug design. Therefore, it is a fundamental challenge to identify viral proteins according to their amino acid sequences in the field of biopharmaceuticals. The traditional prediction methods su er from the data imbalance problem and take too long computation time. To this end, this paper proposes a deep learning framework for virus protein identifying. In the framework, we employ Temporal Convolutional Network(TCN) instead of Recurrent Neural Network(RNN) for feature extraction to improve computation e ciency. We also customize the cost-sensitive loss function of TCN and introduce the misclassification cost of training samples into the weight update of Gradient Boosting Decision Tree(GBDT) to address data imbalance problem. Experiment results show that our framework not only outperforms traditional data imbalance methods but also greatly reduces the computation time with slight performance enhancement.
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Keywords viral protein identifying; data imbalance; deep learning; TCN; GBDT

Citation: Hanyu Zhao, Chao Che, Bo Jin, Xiaopeng Wei. A viral protein identifying framework based on temporal convolutional network. Mathematical Biosciences and Engineering, 2019, 16(3): 1709-1717. doi: 10.3934/mbe.2019081


  • 1. O. P. Zhirnov, A. L. Ksenofontov and N. D. Klenk, Influenza A virus M1 matrix protein is similar to protease inhibitors, Dokl. Akad. Nauk, 367(1999), 690–693.
  • 2. S. Niu, T. Huang and K. Feng, et al., Prediction of tyrosine sulfation with mRMR feature selection and analysis, J. Proteome Res., 9(2010), 6490–6497.
  • 3. S. Bai, J. Z. Kolter, and V. Koltun, An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, preprint, arXiv:0707.0078.
  • 4. Z. C. Lipton, J. Berkowitz, and C. Elkan, A critical review of recurrent neural networks for sequence learning, preprint, arXiv:1506.00019.
  • 5. J. H. Friedman, Greedy function approximation: A gradient boosting machine, Ann. Stat., 29(2001), 1189–1232.
  • 6. D. Wang, N. K. Lee and T. S. Dillon, et al., Protein sequences classification using radial basis function (RBF) neural networks, In: International Conference on Neural Information Processing, 2(2002), 764–768.
  • 7. C. Lin, Y. Zou and J. Qin, et al., Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier, PLoS One, 8(2013), e56499.
  • 8. T. K. Lee and T. Nguyen, Protein Family Classication with Neural Networks, Stanford University, 2016, Available online: https://cs224d.stanford.edu/reports/LeeNguyen.pdf.
  • 9. H. Li, H. Yu and X. Gong, A Deep Learning Model for Predicting RNA-Binding Proteins Only from Primary Sequences, J. Comput. Res. Dev., 55(2018), 93–101.
  • 10. N. V. Chawla, K. W. Bowyer and L. O. Hall, et al., SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16(2002), 321-357.
  • 11. W. Fan, S. J. Stolfo and J. Zhang, et al., AdaCost: Misclassification Cost-Sensitive Boosting, In: Sixteenth International Conference on Machine Learning, (1999), 97–105.
  • 12. Y. Freund and R.E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, J. Comput. Syst. Sci., 55(1997), 119–139.
  • 13. J. Long, E. Shelhamer and T. Darrell, Fully convolutional networks for semantic segmentation, In: IEEE Conference on Computer Vision and Pattern Recognition, 39(2015), 640–651.
  • 14. T. J. Brazil, Causal-Convolution-A New Method for the Transient Analysis of Linear Systems at Microwave Frequencies, IEEE T. Microw. Theory, 43(1995), 315–323.
  • 15. A. V. D. Oord, S. Dieleman and H. Zen, et al., WaveNet: A Generative Model for Raw Audio, preprint, arXiv:1609.03499.
  • 16. K. He, X. Zhang and S. Ren, et al., Deep Residual Learning for Image Recognition, In: IEEE Conference on Computer Vision and Pattern Recognition, (2016), 770–778.
  • 17. P. Branco, L. Torgo and R. Ribeiro, A survey of predictive modelling under imbalanced distributions. preprint, arXiv:1505.01658.


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