hrg12341 / enacp

EnACP is a method to identify anticancer peptides using diversified feature representations and ensemble learning.

EnACP

EnACP is a method to identify anticancer peptides using diversified feature representations and ensemble learning.

1. Pre-Installation

The sklearn, imblearn and lightgbm packages need to be pre-installed.

If you clone this git repository, you dont need install BioSeq-Analysis package. The package and documents of BioSeq-Analysis are available at http://bioinformatics.hitsz.edu.cn/BioSeq-Analysis/download. Before using BioSeq-Analysis, the Python software should be first installed and configured. Python 2.7 64-bit is recommended, which can be downloaded from https://www.python.org.

2. Usage

 python  EnACP_Predict.py EnACP/Input_data/Input_data_fasta/test/test.fasta

3. Reference and Feedback

Ruiquan Ge, Guanwen Feng, Xiaoyang Jing, Renfeng Zhang, Pu Wang and Qing Wu. EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides. Submitted,2020.

Please contact the development team at: [email protected] or [email protected] to submit questions or feedback for us.