ArticlePloS one2017
Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- DeepIR-Pred: identification of insulin receptors using metaheuristic optimization of biologically informed multi-view features with deep recurrent learning.Briefings in bioinformatics · 2026Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Enhancing cross-domain protein and peptide interaction with retrained deep learning models.Briefings in bioinformatics · 2025Article
- Predicting Affinity Through Homology (PATH): Interpretable binding affinity prediction with persistent homology.PLoS computational biology · 2025Article
- Article
- Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.Biomolecules · 2025Review
- TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.Briefings in bioinformatics · 2024Article
- ToxTeller: Predicting Peptide Toxicity Using Four Different Machine Learning Approaches.ACS omega · 2024Article
- Protein-Protein Interfaces: A Graph Neural Network Approach.International journal of molecular sciences · 2024Article
- Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.International journal of molecular sciences · 2024Article
- DPI_CDF: druggable protein identifier using cascade deep forest.BMC bioinformatics · 2024Article
- MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction.Frontiers in genetics · 2024Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Graph-BERT and language model-based framework for protein-protein interaction identification.Scientific reports · 2023Article
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- Graph Neural Network for Protein-Protein Interaction Prediction: A Comparative Study.Molecules (Basel, Switzerland) · 2022Article
- Prediction of protein-protein interaction using graph neural networks.Scientific reports · 2022Article
- Individualized prediction of chronic kidney disease for the elderly in longevity areas in China: Machine learning approaches.Frontiers in public health · 2022Article
- Phage_UniR_LGBM: Phage Virion Proteins Classification with UniRep Features and LightGBM Model.Computational and mathematical methods in medicine · 2022Article
- Predicting Prolonged Length of ICU Stay through Machine Learning.Diagnostics (Basel, Switzerland) · 2021Article
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5 authors.
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Abstract
Nowadays a number of computational approaches have been developed to effectively and accurately predict protein interactions. However, most of these methods typically perform worse when other biological data sources (e.g., protein structure information, protein domains, or gene neighborhoods information) are not available. In the present work, we propose a method for predicting protein interactions making full use of physicochemical characteristics of amino acids. A protein sequence is encoded at multi-scale by seven properties, including their qualitative and quantitative descriptions, of amino acids. Five kinds of protein descriptors, frequency, composition, transformation, distribution and auto covariance, are extracted from these encodings for representing each protein sequence. The new formed feature representation consisted of 347 dimensions is able to capture not only the compositional and positional information but also their statistical significance of amino acids in the sequence. Based on such a feature representation, the gradient boosting decision tree algorithm is introduced to predict protein interaction class. When the proposed method is tested with the PPI data of S.cerevisiae, it achieves a prediction accuracy of 95.28% at the Matthew's correlation coefficient of 90.68%. Compared with the state-of-the-art works on H.pylori and Human, the accuracies can be raised to 89.27% and 98.00% respectively. Extensive experiments are performed for a crossover protein-protein interactions network and the prediction accuracies are also very promising. Because of learning capabilities of the gradient boosting decision tree and the mutil-scale feature representation scheme, the proposed method might be a useful tool for future proteomics studies.
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