DNE

Deep representation learning of protein-protien interaction networks for enhanced pattern discovery

Science Advances, 2024. HTML | PDF | Cite

TL;DR: Python implementation of DNE proposed in our paper.

• The proposed method, referred to as discriminative network embedding (DNE), characterizes a node in the network both locally and globally by harnessing the contrast between representations from neighboring and distant nodes.

• DNE substantially outperforms existing techniques across various critical biological network analyses, including the inference of biological interactions and the identification of functional modules.
• DNE uniquely improves network embedding by incorporating protein sequence features from pre-trained protein language models, resulting in a substantial enhancement in PPI prediction accuracy.

Table of Contents

• Set up the Conda Environment
• Unsupervised pre-training on network data using DNE
• Downstream analysis with learned node embeddings
• Example Jupyter notebooks for using DNE

Set Up the Conda Environment

git clone https://github.com/rui-yan/DNE.git
cd DNE
conda env create -f dne_conda.yml
conda activate dne

• NVIDIA GPU (Tested on Nvidia Quadro RTX 8000 48G x 1) on local workstations
• Python (3.9.20), torch (2.5.1), torch-geometric (2.6.1), networkx (3.2.1), numpy (1.26.4), pandas (2.2.3), scikit-learn (1.5.2), scipy (1.13.1); For further details on the software and package versions used, please refer to the dne_conda.yml file.

Unsupervised pre-training on network data using DNE

from models.dne import DNE
from dataset import GraphDataset

graph_data = GraphDataset(data_path)
graph_data.load_graph(dataset)
graph = graph_data.graph

# Build and train a DNE model using the simulated walks
model = DNE(graph, hidden_dim=128)
model.train(batch_size=5120, epochs=10)

# Extract embeddings from the pre-trained DNE model
embeddings = model.get_embeddings()

Downstream analysis with learned node embeddings

The code for performing various downstream tasks using the learned embeddings is available at code/tasks.

Example Jupyter notebooks for using DNE

• Link prediction: Please see our example notebook in demo.ipynb.

Citation

If you find our work helpful in your research or if you use any source codes, please cite our paper.

@article{yan2024deep,
  title={Deep representation learning of protein-protein interaction networks for enhanced pattern discovery},
  author={Yan, Rui and Islam, Md Tauhidul and Xing, Lei},
  journal={Science Advances},
  volume={10},
  number={51},
  pages={eadq4324},
  year={2024},
  publisher={American Association for the Advancement of Science}
}