ArticleBMC bioinformatics2019
Protein-protein interaction prediction using a hybrid feature representation and a stacked generalization scheme.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and embedding-based representations.Scientific reports · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Targeting protein protein interactions and their modulators to enable new therapeutic strategies for human diseases.NPJ systems biology and applications · 2026Review
- Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management.Veterinary sciences · 2025Review
- Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process.Scientific reports · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions.bioRxiv : the preprint server for biology · 2024Article
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- A New Tool to Study the Binding Behavior of Intrinsically Disordered Proteins.International journal of molecular sciences · 2023Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
- Prediction of viral-host interactions of COVID-19 by computational methods.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Multi-view heterogeneous molecular network representation learning for protein-protein interaction prediction.BMC bioinformatics · 2022Article
- Computational Network Inference for Bacterial Interactomics.mSystems · 2022Review
- Molecular fusion events in carcinogenic organisms: a bioinformatics study for the detection of fused proteins between viruses, bacteria and eukaryotes.EMBnet.journal · 2022Article
- Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods.Pathogens (Basel, Switzerland) · 2022Article
- A multitask transfer learning framework for the prediction of virus-human protein-protein interactions.BMC bioinformatics · 2021Article
- Article
- Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization.Journal of chemical information and modeling · 2021Article
- A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.Interdisciplinary sciences, computational life sciences · 2021Article
- Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy.Frontiers in bioinformatics · 2021Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough various machine learning-based predictors have been developed for estimating protein-protein interactions, their performances vary with dataset and species, and are affected by two primary aspects: choice of learning algorithm, and the representation of protein pairs. To improve the performance of predicting protein-protein interactions, we exploit the synergy of multiple learning algorithms, and utilize the expressiveness of different protein-pair features.
resultsWe developed a stacked generalization scheme that integrates five learning algorithms. We also designed three types of protein-pair features based on the physicochemical properties of amino acids, gene ontology annotations, and interaction network topologies. When tested on 19 published datasets collected from eight species, the proposed approach achieved a significantly higher or comparable overall performance, compared with seven competitive predictors.
conclusionWe introduced an ensemble learning approach for PPI prediction that integrated multiple learning algorithms and different protein-pair representations. The extensive comparisons with other state-of-the-art prediction tools demonstrated the feasibility and superiority of the proposed method.
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