ArticleInternational journal of molecular sciences2015
Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors.
Article in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns.iScience · 2026Article
- Predicting Protein-Protein Interactions by Convolutional Neural Network Model.Biotech (Basel (Switzerland)) · 2026Article
- Enhancing cross-domain protein and peptide interaction with retrained deep learning models.Briefings in bioinformatics · 2025Article
- Predicting protein-protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information.Frontiers in pharmacology · 2025Article
- MGPPI: multiscale graph neural networks for explainable protein-protein interaction prediction.Frontiers in genetics · 2024Article
- Graph-BERT and language model-based framework for protein-protein interaction identification.Scientific reports · 2023Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Prediction of protein-protein interaction using graph neural networks.Scientific reports · 2022Article
- An effective drug-disease associations prediction model based on graphic representation learning over multi-biomolecular network.BMC bioinformatics · 2022Article
- Graph regularized LBMC bioinformatics · 2020Article
- Using discriminative vector machine model with 2DPCA to predict interactions among proteins.BMC bioinformatics · 2019Article
- Multimodal deep representation learning for protein interaction identification and protein family classification.BMC bioinformatics · 2019Article
- An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model.International journal of molecular sciences · 2019Article
- GIMDA: Graphlet interaction-based MiRNA-disease association prediction.Journal of cellular and molecular medicine · 2018Article
- Article
- Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization.Frontiers in genetics · 2018Article
- Detection of Interactions between Proteins by Using Legendre Moments Descriptor to Extract Discriminatory Information Embedded in PSSM.Molecules (Basel, Switzerland) · 2017Article
- RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction.RNA biology · 2017Article
- PCVMZM: Using the Probabilistic Classification Vector Machines Model Combined with a Zernike Moments Descriptor to Predict Protein-Protein Interactions from Protein Sequences.International journal of molecular sciences · 2017Article
- Article
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6 authors.
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Abstract
Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori, we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.
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