EnACP is a model designed to identify anticancer peptides by utilizing diverse feature representations and ensemble learning techniques.
Note: All files should be unzipped before running this project.
Before using EnACP, make sure to install the necessary packages:
sklearnimblearnlightgbm
If you’ve cloned this repository, you won’t need to install the BioSeq-Analysis package separately. You can find the package and its documentation at BioSeq-Analysis Download. Ensure that Python 2.7 (64-bit) is installed and configured, which can be downloaded from Python Official Site.
To run the model, use the following command:
python EnACP_Predict.py EnACP/Input_data/Input_data_fasta/test/test.fasta
This version is properly formatted for `README.md` with clear headers, sections, and proper Markdown syntax. It includes:
1. A model introduction with a note about unzipping the files.
2. Instructions on pre-installation and package dependencies.
3. A command for running the model.
4. A reference and feedback section with contributor names.
5. Contact information with clickable links for email, website, Kaggle, and LinkedIn.
## 3. Reference and Feedback
### Contributors:
- Ruiquan Ge (葛瑞权)
- Jimin (智旻)
- Guanwen Feng (冯冠文)
- Xiaoyang Jing (景晓阳)
- Renfeng Zhang (张仁峰)
- Pu Wang (王璞)
- Qing Wu (吴清)
## Contact Information
**Musfique LLC**
Address: 7901 4TH STY # 11417 ST.PETERSBURG, FL, US 33702
Contact: (BDT+) 01798269968
Email: [info@musfiquellc.com](mailto:info@musfiquellc.com)
Website: [www.musfiquellc.com](http://www.musfiquellc.com)
Kaggle: [musfiquejim](https://www.kaggle.com/musfiquejim)