ArticleFrontiers in genetics2021
A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Cheminformatic analysis of protein surfaces provides binding site insights and informs identification strategies.Future medicinal chemistry · 2025Article
- GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions.BMC bioinformatics · 2025Article
- Feature fusion with attributed deepwalk for protein-protein interaction prediction.Scientific reports · 2025Article
- Ayu: a machine intelligence tool for identification of extracellular proteins in the marine secretome.Nature communications · 2025Article
- Machine Learning Methods in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Identification of novel interacts partners of ADAR1 enzyme mediating the oncogenic process in aggressive breast cancer.Scientific reports · 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
4 authors.
Funding
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Abstract
Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.
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