ArticleBioinformatics (Oxford, England)2019
Protein-protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns.iScience · 2026Article
- SPPIPred: Stacking-based ensemble learning model for identification of protein-protein interaction.PloS one · 2026Article
- Tree-based ensemble learning models for protein-protein interactions detection: a review and experimental evaluation.BioData mining · 2025Review
- MED-PPIS: Multi-order Moments External Graph Attention Network with Dual-Axis Attention for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- BERT-DomainAFP: Antifreeze protein recognition and classification model based on BERT and structural domain annotation.iScience · 2025Article
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- Article
- Pair-EGRET: enhancing the prediction of protein-protein interaction sites through graph attention networks and protein language models.Bioinformatics (Oxford, England) · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.BMC bioinformatics · 2023Article
- Article
- Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach.Journal of cancer research and clinical oncology · 2023Article
- Accelerating the Screening of Small Peptide Ligands by Combining Peptide-Protein Docking and Machine Learning.International journal of molecular sciences · 2023Article
- Golgi_DF: Golgi proteins classification with deep forest.Frontiers in neuroscience · 2023Article
- Accurate Prediction of Anti-hypertensive Peptides Based on Convolutional Neural Network and Gated Recurrent unit.Interdisciplinary sciences, computational life sciences · 2022Article
- Article
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
- Using amino acids co-occurrence matrices and explainability model to investigate patterns in dengue virus proteins.BMC bioinformatics · 2022Article
- Protein-Protein Docking: Past, Present, and Future.The protein journal · 2022Review
- DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.Frontiers in bioengineering and biotechnology · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationThe prediction of protein-protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples.
resultsA new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2-15.7% and 6.1-18.9% higher than the other existing tools, respectively. AVAILABILITY AND IMPLEMENTATION: The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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