ArticleEvolutionary bioinformatics online2019
Sequence-based Prediction of Protein-Protein Interactions Using Gray Wolf Optimizer-Based Relevance Vector Machine.
Article in Evolutionary bioinformatics online, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 24 citations in OpenAlex.
- An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns.iScience · 2026Article
- Predicting protein-protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information.Frontiers in pharmacology · 2025Article
- Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance.PeerJ. Computer science · 2025Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- Robust and accurate prediction of self-interacting proteins from protein sequence information by exploiting weighted sparse representation based classifier.BMC bioinformatics · 2022Article
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization.Journal of chemical information and modeling · 2021Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
- Predicting Self-Interacting Proteins Using a Recurrent Neural Network and Protein Evolutionary Information.Evolutionary bioinformatics online · 2020Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) are essential to a number of biological processes. The PPIs generated by biological experiment are both time-consuming and expensive. Therefore, many computational methods have been proposed to identify PPIs. However, most of these methods are limited as they are difficult to compute and rely on a large number of homologous proteins. Accordingly, it is urgent to develop effective computational methods to detect PPIs using only protein sequence information. The kernel parameter of relevance vector machine (RVM) is set by experience, which may not obtain the optimal solution, affecting the prediction performance of RVM. In this work, we presented a novel computational approach called GWORVM-BIG, which used Bi-gram (BIG) to represent protein sequences on a position-specific scoring matrix (PSSM) and GWORVM classifier to perform classification for predicting PPIs. More specifically, the proposed GWORVM model can obtain the optimum solution of kernel parameters using gray wolf optimizer approach, which has the advantages of less control parameters, strong global optimization ability, and ease of implementation compared with other optimization algorithms. The experimental results on
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