GitHub - kouzheng/BIVPred-FL

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Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
feature		feature
model		model
AAfactor.csv		AAfactor.csv
BIVPred_FL.py		BIVPred_FL.py
OLP-BIT-position.csv		OLP-BIT-position.csv
OLP.csv		OLP.csv
f1_cl.csv		f1_cl.csv
f2_cl.csv		f2_cl.csv
f3_cl.csv		f3_cl.csv
f4_cl.csv		f4_cl.csv
f5_cl.csv		f5_cl.csv
feature.py		feature.py
feature.pyc		feature.pyc
ft_hot.csv		ft_hot.csv
ft_val.csv		ft_val.csv
mrmr_hot.csv		mrmr_hot.csv
mrmr_val.csv		mrmr_val.csv
pred_hot.csv		pred_hot.csv
pred_val.csv		pred_val.csv
predic_fl.py		predic_fl.py
predic_fl.pyc		predic_fl.pyc
predicting_results.csv		predicting_results.csv
predictor.py		predictor.py
predictor.pyc		predictor.pyc
readme.txt		readme.txt
sample.fasta		sample.fasta

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1. The code is design based on Python 2.7.15 and Scikit-learn 0.20.4 with the dependence of Biopython 1.72,Numpy 1.16.5,Pandas 0.24.2.
2. The input sequence should be fasta format with 40 amino acids£¬
3. "BIVPred_FL.py" is the main file.
4. "predicting_results.csv" is the result file for virulence.
5. The folder "feature" contains all of the 67 features of your sequences.
6. The folder "model" contains the trained model based on random forest algorism and our dataset.
7. The predicted label for 'P' means the pandemic risk, while label for 'N' means not.

Reference
Kou, Z., Fan, X., Li, J. et al. Using amino acid features to identify the pathogenicity of influenza B virus. Infect Dis Poverty 11, 50 (2022). 
https://doi.org/10.1186/s40249-022-00974-0