ArticleMolecules (Basel, Switzerland)2022
Graph Neural Network for Protein-Protein Interaction Prediction: A Comparative Study.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks.Journal of chemical information and modeling · 2026Article
- A multimodal human protein embeddings database: DeepDrug Protein Embeddings Bank (DPEB).NAR genomics and bioinformatics · 2026Article
- Genome-wide identification of maize G protein genes and regulatory roles of the ZmGG1 subfamily in saline-alkali stress response.BMC plant biology · 2026Article
- gPPIpred: A User-Friendly PPI Predictor Based on Protein Molecular Graphs.microPublication biology · 2026Article
- Bioactive peptides targeting dyslipidemia and atherosclerosis: from computational discovery and data bottlenecks to precision medicine translation.Frontiers in molecular biosciences · 2026Review
- Prediction of protein-protein interaction based on interaction-specific learning and hierarchical information.BMC biology · 2025Article
- Integrating Redox Proteomics and Computational Modeling to Decipher Thiol-Based Oxidative Post-Translational Modifications (oxiPTMs) in Plant Stress Physiology.International journal of molecular sciences · 2025Review
- GRANet: a graph residual attention network for gene regulatory network inference.Briefings in bioinformatics · 2025Article
- Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks.Frontiers in bioinformatics · 2025Article
- SpatialPPIv2: Enhancing protein-protein interaction prediction through graph neural networks with protein language models.Computational and structural biotechnology journal · 2025Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.Communications biology · 2024Article
- Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.International journal of molecular sciences · 2023Article
- PPIntegrator: semantic integrative system for protein-protein interaction and application for host-pathogen datasets.Bioinformatics advances · 2023Article
- Fast end-to-end surface interpretation of SARS-CoV-2 variants by differentiable molecular surface interaction fingerprinting method.Computational and structural biotechnology journal · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein-protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein-protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.
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