ArticleBioMed research international2016
Ens-PPI: A Novel Ensemble Classifier for Predicting the Interactions of Proteins Using Autocovariance Transformation from PSSM.
Article in BioMed research international, 2016. 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.
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- MSPEDTI: Prediction of Drug-Target Interactions via Molecular Structure with Protein Evolutionary Information.Biology · 2022Article
- Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information.Evolutionary bioinformatics online · 2020Article
- Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest.Scientific reports · 2019Article
- An Improved Deep Forest Model for Predicting Self-Interacting Proteins From Protein Sequence Using Wavelet Transformation.Frontiers in genetics · 2019Article
- Handling Noise in Protein Interaction Networks.BioMed research international · 2019Article
- Using Two-dimensional Principal Component Analysis and Rotation Forest for Prediction of Protein-Protein Interactions.Scientific reports · 2018Article
- An ensemble approach for large-scale identification of protein- protein interactions using the alignments of multiple sequences.Oncotarget · 2017Article
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Authors and funding
6 authors.
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Abstract
Protein-Protein Interactions (PPIs) play vital roles in most biological activities. Although the development of high-throughput biological technologies has generated considerable PPI data for various organisms, many problems are still far from being solved. A number of computational methods based on machine learning have been developed to facilitate the identification of novel PPIs. In this study, a novel predictor was designed using the Rotation Forest (RF) algorithm combined with Autocovariance (AC) features extracted from the Position-Specific Scoring Matrix (PSSM). More specifically, the PSSMs are generated using the information of protein amino acids sequence. Then, an effective sequence-based features representation, Autocovariance, is employed to extract features from PSSMs. Finally, the RF model is used as a classifier to distinguish between the interacting and noninteracting protein pairs. The proposed method achieves promising prediction performance when performed on the PPIs of Yeast, H. pylori, and independent datasets. The good results show that the proposed model is suitable for PPIs prediction and could also provide a useful supplementary tool for solving other bioinformatics problems.
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