ArticleScientific reports2021
Robust and accurate prediction of protein-protein interactions by exploiting evolutionary information.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 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
- DA-HGL: a domain-augmented heterogeneous graph learning framework for protein function prediction.Briefings in bioinformatics · 2025Article
- Feature fusion with attributed deepwalk for protein-protein interaction prediction.Scientific reports · 2025Article
- Prediction of inhibitory peptides against E.coli with desired MIC value.Scientific reports · 2025Article
- Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance.PeerJ. Computer science · 2025Article
- Predicting protein-protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information.Frontiers in pharmacology · 2025Article
- Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process.Scientific reports · 2024Article
- Protein features fusion using attributed network embedding for predicting protein-protein interaction.BMC genomics · 2024Article
- Protein-protein interaction site prediction by model ensembling with hybrid feature and self-attention.BMC bioinformatics · 2023Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.BMC bioinformatics · 2022Article
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
- The Importance of Weakly Co-Evolving Residue Networks in Proteins is Revealed by Visual Analytics.Frontiers in bioinformatics · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Various biochemical functions of organisms are performed by protein-protein interactions (PPIs). Therefore, recognition of protein-protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.
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