ArticleScientific reports2017
Different protein-protein interface patterns predicted by different machine learning methods.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Structural Descriptors for Subunit Interface Regions in Homodimers: Effect of Lipid Membrane and Secondary Structure Type.Journal of chemical information and modeling · 2025Article
- Prediction of the Trimer Protein Interface Residue Pair by CNN-GRU Model Based on Multi-Feature Map.Nanomaterials (Basel, Switzerland) · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Prediction of the tetramer protein complex interaction based on CNN and SVM.Frontiers in genetics · 2023Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Structural Design and Analysis of the RHOA-ARHGEF1 Binding Mode: Challenges and Applications for Protein-Protein Interface Prediction.Frontiers in molecular biosciences · 2021Article
- A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers.Molecules (Basel, Switzerland) · 2020Article
- Neurocardiac regulation: from cardiac mechanisms to novel therapeutic approaches.The Journal of physiology · 2020Review
- Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction.BMC bioinformatics · 2019Article
- Article
- Pattern to Knowledge: Deep Knowledge-Directed Machine Learning for Residue-Residue Interaction Prediction.Scientific reports · 2018Article
- Review
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.
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