SAMP: a Split amino acid composition based AntiMicrobial Prediction model
SAMP is an ensemble random projection (RP) based computational model that leverages a new type of features called proportionalized split amino acid composition (PSAAC) in addition to conventional sequence-based features for AMP prediction. SAMP also incorporates the ensemble RP architecture to process large scale AMP screening.
Preprint: Junxi Feng, Mengtao Sun, Cong Liu, Weiwei Zhang, Changmou Xu, Jieqiong Wang, Guangshun Wang, Shibiao Wan "SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition. bioRxiv, 2024.04.25.590553v1 (2024)."
Provide detailed installation steps:
- Clone the repository.
git clone https://github.com/wan-mlab/SAMP.git cd SAMP - Create conda environment.
conda env create -f environment.yml conda activate SAMP
- Now you can run the Tutorial.ipynb and use SAMP with your own dataset!
See Tutorial.ipynb