Python 3.8.3 and Pytorch 1.12.1 implementation of the Signed Two-Space Proximity Model for Learning Representations in Protein-Protein Interaction Networks.

Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates positive and negative interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes, representing extreme protein profiles. S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns.

conda create -n ${env_name} python=3.8.3  

conda activate ${env_name} 

pip install -r requirements.txt

Our Pytorch implementation uses the pytorch_sparse package. Installation guidelines can be found at the corresponding Github repository.

pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.12.1+cpu.html

pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.12.1+${CUDA}.html

where ${CUDA} should be replaced by either cu102, cu113, or cu116 depending on your PyTorch installation.

RUN:   python main.py

optional arguments:

--epochs   number of epochs for training (default: 5K)

--cuda   CUDA training (default: True)

--LP   performs link prediction (default: True)

--D   dimensionality of the embeddings (default: 8)

--lr   learning rate for the ADAM optimizer (default: 0.05)

--dataset   dataset to apply S2SPM on (default: Sapiens)

--sample_percentage   sample size network percentage, it should be equal or less than 1 (default: 0.3)

RUN:   python main.py --cuda False --D 8 --pretrained False

The code has been primarily constructed and optimized for running in a GPU-enabled environment, running the code in CPU is significantly slower.

The signed two-space proximity model for learning representations in protein–protein interaction networks, OUP Bioinformatics 2025.