SAMP : a Split amino acid composition based AntiMicrobial Peptides 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.

Publication

SAMP: Identifying antimicrobial peptides by an ensemble learning model based on proportionalized split amino acid composition. Junxi Feng, Mengtao Sun, Cong Liu, Weiwei Zhang, Changmou Xu, Jieqiong Wang, Guangshun Wang and Shibiao Wan. Briefings in Functional Genomics, 2024, 23, 879–890. DOI:https://doi.org/10.1093/bfgp/elae046

Table of Contents (Under construction)

• Installation
• Usage
• Contribute
• Tests
• License
• Contact

Installation

Provide detailed installation steps:

1. Clone the repository.

   git clone https://github.com/wan-mlab/SAMP.git
   cd SAMP

2. Create conda environment.

   conda env create -f environment.yml
   conda activate SAMP

3. Now you can run the Tutorial.ipynb and use SAMP with your own dataset!

Usage

See Tutorial.ipynb