ArticleBioMed research international2015
Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and embedding-based representations.Scientific reports · 2026Article
- SPPIPred: Stacking-based ensemble learning model for identification of protein-protein interaction.PloS one · 2026Article
- Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.NPJ science of food · 2025Article
- Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance.PeerJ. Computer science · 2025Article
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.International journal of molecular sciences · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
- Improved global protein homolog detection with major gains in function identification.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- ProtInteract: A deep learning framework for predicting protein-protein interactions.Computational and structural biotechnology journal · 2023Article
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA-miRNA interaction prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- Prediction of Antibody-Antigen Binding via Machine Learning: Development of Data Sets and Evaluation of Methods.JMIR bioinformatics and biotechnology · 2022Article
- ADH-PPI: An attention-based deep hybrid model for protein-protein interaction prediction.iScience · 2022Article
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
- GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.BMC genomics · 2022Article
- Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network machine learning model.BMC bioinformatics · 2022Article
- Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset of Yeast, Human, and H. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets.
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