Protein Classification using Graph Neural Networks (GNNs)

Introduction

This repository contains the implementation of a Graph Neural Network (GNN) model for classifying proteins into two categories: enzymes and non-enzymes. The project aims to leverage the power of GNNs to provide accurate and efficient protein classification.

Requirements

• Python 3.x
• PyTorch
• NetworkX
• scikit-learn
• Jupyter Notebook

Methods

The model uses a Graph Neural Network to classify proteins based on their amino acid composition, protein length, and other physicochemical properties.

Results

The GNN model achieved an accuracy of approximately 73.33%. For more detailed performance metrics, please refer to the Jupyter Notebook.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Acknowledgments

• Paper: Protein function prediction via graph kernels