/GNNs-for-protein-protein-interaction

Primary LanguagePython

Exploration of Graph Attention Network architecture and Graph Convolution architecture to classify nodes in Protein-Protein Interaction dataset.
Implemented in pytorch.

Requirements:

List of packages is provided in the requirements.txt To install pytorch-geometric run:

pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+${CUDA}.html
pip install torch-geometric

where ${CUDA} should be replaced by either cpu, cu92, cu101, cu102, or cu110 depending on your PyTorch installation.

How to run:

  1. Install the requirements stated in the requirements.txt
  2. To run the train script use the following command:
python train.py --model_type=<'GAT' for Graph Attention Network, 'GCN' for Graph Convolution network architecture>  
                --input_dir=<directory containing input data, e.g. 'data/ppi_dataset'>  
                --output_dir=<dir to save the model, e.g. 'saved_models'>
  1. To evaluate a model on the test set use the following command:
python evaluate.py --model_type=<'GAT' for Graph Attention Network, 'GCN' for Graph Convolution network architecture>  
                   --input_dir=<directory containing input data, e.g. 'data/ppi_dataset'>  
                   --model_path=<directory of the model file, e.g.'saved_models/best_model_GAT.pt'>