ArticleBMC bioinformatics2017
Sequence-based prediction of protein protein interaction using a deep-learning algorithm.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers.
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Who cites it
129 citing papers in PubMed.
- A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and embedding-based representations.Scientific reports · 2026Article
- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
- Quantum-augmented graph differential geometry enhances accuracy in protein-protein interaction prediction.Scientific reports · 2026Article
- Accurate proteome-wide prediction of enzymes and catalytic sites using graph deep learning and protein language model.GigaScience · 2026Article
- Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction.Computational and structural biotechnology journal · 2026Article
- gPPIpred: A User-Friendly PPI Predictor Based on Protein Molecular Graphs.microPublication biology · 2026Article
- Component puzzle protein-protein interaction prediction.Briefings in bioinformatics · 2025Article
- GNNenrich: a novel method for pathway enrichment analysis based on graph neural network.Bioinformatics (Oxford, England) · 2025Article
- DensePPI-2: a bio-inspired update for sequence-based PPI prediction leveraging mutation rates.Briefings in bioinformatics · 2025Article
- An interpretable deep geometric learning model to predict the effects of mutations on protein-protein interactions using large-scale protein language model.Journal of cheminformatics · 2025Article
- Carmna: classification and regression models for nitrogenase activity based on a pretrained large protein language model.Briefings in bioinformatics · 2025Article
- Structure-Based Deep Learning Framework for Modeling Human-Gut Bacterial Protein Interactions.Proteomes · 2025Article
- Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.Biomolecules · 2025Review
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein-Protein Interaction Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- NFEmbed: modeling nitrogenase activity via classification and regression with pretrained protein embeddings.Bioinformatics advances · 2025Article
- Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions.Computational and structural biotechnology journal · 2025Article
- SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer.Computational and structural biotechnology journal · 2024Article
- BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning.Computational and structural biotechnology journal · 2024Article
- Graph-based machine learning model for weight prediction in protein-protein networks.BMC bioinformatics · 2024Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProtein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested.
resultsWe used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods.
conclusionsTo our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.
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