ArticleInternational journal of molecular sciences2020
Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Protein-Protein Interaction Site Prediction via EquiPPIS and Its Application in Studying Viral Replication.Methods in molecular biology (Clifton, N.J.) · 2026Article
- ASCE-PPIS: a protein-protein interaction sites predictor based on equivariant graph neural network with fusion of structure-aware pooling and graph collapse.Bioinformatics (Oxford, England) · 2025Article
- Predicting hospital outpatient volume using XGBoost: a machine learning approach.Scientific reports · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.Briefings in bioinformatics · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- MGMA-PPIS: Predicting the protein-protein interaction site with multiview graph embedding and multiscale attention fusion.GigaScience · 2025Article
- Prediction of drug target interaction based on under sampling strategy and random forest algorithm.PloS one · 2025Article
- Article
- Discovery of Antimicrobial Lysins from the "Dark Matter" of Uncharacterized Phages Using Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- MEG-PPIS: a fast protein-protein interaction site prediction method based on multi-scale graph information and equivariant graph neural network.Bioinformatics (Oxford, England) · 2024Article
- E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.PLoS computational biology · 2023Article
- Recent Advances in Biomolecular Recognition.International journal of molecular sciences · 2023Article
- PITHIA: Protein Interaction Site Prediction Using Multiple Sequence Alignments and Attention.International journal of molecular sciences · 2022Article
- Article
- An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties.BMC bioinformatics · 2021Article
- A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.Frontiers in genetics · 2021Article
- LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.International journal of molecular sciences · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of protein interactions have been confirmed by experimental techniques, which greatly affects the predictive capability of computational methods. In this work, two imbalanced data processing strategies based on XGBoost algorithm were proposed to re-balance the original dataset from inherent relationship between positive and negative samples for the prediction of protein-protein interaction sites. Herein, a feature extraction method was applied to represent the protein interaction sites based on evolutionary conservatism of proteins, and the influence of overlapping regions of positive and negative samples was considered in prediction performance. Our method showed good prediction performance, such as prediction accuracy of 0.807 and MCC of 0.614, on an original dataset with 10,455 surface residues but only 2297 interface residues. Experimental results demonstrated the effectiveness of our XGBoost-based method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.