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Mathematical Biosciences and Engineering

2019, Volume 16, Issue 3: 1709-1717. doi: 10.3934/mbe.2019081
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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
  • Received: 17 December 2018 Accepted: 31 January 2019 Published: 27 February 2019

  • 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.

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

    Related Papers:

  • 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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  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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