The offical code of paper :ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides.
cross_val.py is utilized for ACP740 and ACP240 datasets, and independent.py is for Main and Alternate dataset
1、feature extraction
peptide level features should be extracted by http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download.
resdual level features should be extracted by the tool in the folder named "Residual level".
2、put the files from above into a folder as following form：
ACP740: Anti-cancer-data/ACP740/xxx.csv
ACP240: Anti-cancer-data/ACP240/xxx.csv
Main: Anti-cancer-data/Main/xxx.csv
Alternate: Anti-cancer-data/Alternate/xxx.csv
3、train your model using command:
ACP740: python cross_val.py --data_name ACP740 --begin 3
ACP240: python cross_val.py --data_name ACP240 --begin 3
Main: python independent.py --data_name Main --begin 3
Alternate: python independent.py --data_name Alternate --begin 3