ArticleScientific reports2019
Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Examine neural network models for protein sequencing based features prediction.Journal of computer-aided molecular design · 2026Article
- Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives.Proteomes · 2025Review
- A protein dynamics-based deep learning model enhances predictions of fitness and epistasis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- GTE-PPIS: a protein-protein interaction site predictor based on graph transformer and equivariant graph neural network.Briefings in bioinformatics · 2025Article
- PPILS: Protein-protein interaction prediction with language of biological coding.Computers in biology and medicine · 2025Article
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- Accurate Prediction of Protein-Binding Residues in Protein Sequences Using SCRIBER.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Statistical analysis of sequential motifs at biologically relevant protein-protein interfaces.Computational and structural biotechnology journal · 2024Article
- Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.BMC medical informatics and decision making · 2024Article
- LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.Interdisciplinary sciences, computational life sciences · 2024Article
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- Machine Learning Methods in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein-protein interactions.Plant methods · 2023Article
- Generative models for protein sequence modeling: recent advances and future directions.Briefings in bioinformatics · 2023Review
- IRAA: A statistical tool for investigating a protein-protein interaction interface from multiple structures.Protein science : a publication of the Protein Society · 2023Article
- GDockScore: a graph-based protein-protein docking scoring function.Bioinformatics advances · 2023Article
- Prediction of viral-host interactions of COVID-19 by computational methods.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- SIPGCN: A Novel Deep Learning Model for Predicting Self-Interacting Proteins from Sequence Information Using Graph Convolutional Networks.Biomedicines · 2022Article
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Authors and funding
5 authors.
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Abstract
Protein is an essential component of the living organism. The prediction of protein-protein interactions (PPIs) has important implications for understanding the behavioral processes of life, preventing diseases, and developing new drugs. Although the development of high-throughput technology makes it possible to identify PPIs in large-scale biological experiments, it restricts the extensive use of experimental methods due to the constraints of time, cost, false positive rate and other conditions. Therefore, there is an urgent need for computational methods as a supplement to experimental methods to predict PPIs rapidly and accurately. In this paper, we propose a novel approach, namely CNN-FSRF, for predicting PPIs based on protein sequence by combining deep learning Convolution Neural Network (CNN) with Feature-Selective Rotation Forest (FSRF). The proposed method firstly converts the protein sequence into the Position-Specific Scoring Matrix (PSSM) containing biological evolution information, then uses CNN to objectively and efficiently extracts the deeply hidden features of the protein, and finally removes the redundant noise information by FSRF and gives the accurate prediction results. When performed on the PPIs datasets Yeast and Helicobacter pylori, CNN-FSRF achieved a prediction accuracy of 97.75% and 88.96%. To further evaluate the prediction performance, we compared CNN-FSRF with SVM and other existing methods. In addition, we also verified the performance of CNN-FSRF on independent datasets. Excellent experimental results indicate that CNN-FSRF can be used as a useful complement to biological experiments to identify protein interactions.
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