tetano
Editor, Senior Moderator
Infect Dis Poverty
. 2021 Oct 24;10(1):128.
doi: 10.1186/s40249-021-00912-6.
Prediction of pandemic risk for animal-origin coronavirus using a deep learning method
Zheng Kou[SUP] 1 [/SUP], Yi-Fan Huang[SUP] 2 [/SUP], Ao Shen[SUP] 2 [/SUP], Saeed Kosari[SUP] 2 [/SUP], Xiang-Rong Liu[SUP] 3 [/SUP], Xiao-Li Qiang[SUP] 2 [/SUP]
Affiliations
Abstract
Background: 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.
Keywords: Coronavirus; Deep learning; Pandemic risk; Viral genome.
. 2021 Oct 24;10(1):128.
doi: 10.1186/s40249-021-00912-6.
Prediction of pandemic risk for animal-origin coronavirus using a deep learning method
Zheng Kou[SUP] 1 [/SUP], Yi-Fan Huang[SUP] 2 [/SUP], Ao Shen[SUP] 2 [/SUP], Saeed Kosari[SUP] 2 [/SUP], Xiang-Rong Liu[SUP] 3 [/SUP], Xiao-Li Qiang[SUP] 2 [/SUP]
Affiliations
- PMID: 34689829
- DOI: 10.1186/s40249-021-00912-6
Abstract
Background: 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.
Keywords: Coronavirus; Deep learning; Pandemic risk; Viral genome.