ArticleBMC bioinformatics2020
Combining sequence and network information to enhance protein-protein interaction prediction.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Dual-protein embedding-based graph model with dynamic attention for interaction prediction.Briefings in bioinformatics · 2025Article
- The signed two-space proximity model for learning representations in protein-protein interaction networks.Bioinformatics (Oxford, England) · 2025Article
- Protein2Text: Resampling Mechanism to Translate Protein Sequences into Human-Interpretable Text.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2025Article
- Article
- Graph-based machine learning model for weight prediction in protein-protein networks.BMC bioinformatics · 2024Article
- CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.Nature communications · 2024Article
- Deciphering the intricate hierarchical gene regulatory network: unraveling multi-level regulation and modifications driving secondary cell wall formation.Horticulture research · 2024Article
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction.BMC bioinformatics · 2022Article
- SIN-3 functions through multi-protein interaction to regulate apoptosis, autophagy, and longevity in Caenorhabditis elegans.Scientific reports · 2022Article
- Deep learning frameworks for protein-protein interaction prediction.Computational and structural biotechnology journal · 2022Review
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- InfersentPPI: Prediction of Protein-Protein Interaction Using Protein Sentence Embedding With Gene Ontology Information.Frontiers in genetics · 2022Article
- Protein-protein interaction prediction with deep learning: A comprehensive review.Computational and structural biotechnology journal · 2022Review
- Quantitative prediction model for affinity of drug-target interactions based on molecular vibrations and overall system of ligand-receptor.BMC bioinformatics · 2021Article
- Predicting Protein-Protein Interactions via Gated Graph Attention Signed Network.Biomolecules · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundProtein-protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that. Most proteins perform their functions by interacting with other proteins, so predicting PPIs accurately is crucial for understanding cell physiology.
resultsRecently, graph convolutional networks (GCNs) have been proposed to capture the graph structure information and generate representations for nodes in the graph. In our paper, we use GCNs to learn the position information of proteins in the PPIs networks graph, which can reflect the properties of proteins to some extent. Combining amino acid sequence information and position information makes a stronger representation for protein, which improves the accuracy of PPIs prediction.
conclusionIn previous research methods, most of them only used protein amino acid sequence as input information to make predictions, without considering the structural information of PPIs networks graph. We first time combine amino acid sequence information and position information to make representations for proteins. The experimental results indicate that our method has strong competitiveness compared with several sequence-based methods.
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