Prediction of pandemic risk for animal-origin coronavirus using a deep learning method
使用深度学习方法预测动物源性冠状病毒的大流行风险
深層学習法を使用した動物由来コロナウイルスのパンデミックリスクの予測
딥러닝 기법을 이용한 동물 유래 코로나바이러스의 대유행 위험 예측
Predicción del riesgo pandémico de coronavirus de origen animal mediante un método de aprendizaje profundo
Prédiction du risque de pandémie de coronavirus d'origine animale à l'aide d'une méthode d'apprentissage en profondeur
Прогнозирование риска пандемии коронавируса животного происхождения с использованием метода глубокого обучения
¹ Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China
中国 广州 广州大学计算科技研究院
² Department of Computer Science, Xiamen University, Xiamen, 361005, China
中国 厦门 厦门大学计算机科学系
Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes.
Methods
A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models.
Results
The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5–25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor.
Conclusions
Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.
