Open AccessA viral protein identifying framework based on temporal convolutional network
Author(s) -
Hanyu Zhao,
Chao Che,
Bo Jin,
Xiaopeng Wei
Publication year - 2019
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2019081
Subject(s) - computer science , computation , artificial intelligence , boosting (machine learning) , gradient boosting , decision tree , convolutional neural network , machine learning , tree (set theory) , deep learning , recurrent neural network , function (biology) , alternating decision tree , artificial neural network , algorithm , decision tree learning , random forest , incremental decision tree , biology , mathematics , mathematical analysis , evolutionary biology
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.