The model combines a bidirectional GRU with a one-dimensional convolution and uses the genome sequence of coronaviruses as direct input to predict the pandemic risk of human infection.
Guideline
- The code is design based on Python 3.7.4 , tensorflow 2.1.0 and Keras 2.3.1.
- seq_preprocess.py: preprocess the gene sequences of coronavirus.The input file is the full sequences of coronavirus genome as fasta format and should be put into the "fasta" folder.
- predict_CCSI-DL.py: the main file to predict the cross-species transmission risk.The first parameter is the name of query file, the second parameter is the prediction confidence.
- predicting_results.csv shows the result for the prediction of cross-species transmission. The predicted label for 'H' means the phenotype of cross-species transmission while label for 'N' means not.
- The models mentioned in our paper are saved as HDF5 files in "model" folder.
Reference
Kou, Z., Huang, YF., Shen, A. et al. Prediction of pandemic risk for animal-origin coronavirus using a deep learning method. Infect Dis Poverty 10, 128 (2021).
https://doi.org/10.1186/s40249-021-00912-6
https://idpjournal.biomedcentral.com/articles/10.1186/s40249-021-00912-6