ArticlePloS one2014
Prediction of interactions between viral and host proteins using supervised machine learning methods.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 87 citations in OpenAlex.
- 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
- Machine Learning Methods for Protein-Protein Interaction Prediction Based on Noncovalent Interactions.ACS omega · 2026Article
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction.Computational and structural biotechnology journal · 2026Article
- Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.Genomics, proteomics & bioinformatics · 2024Review
- Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions.Computational and structural biotechnology journal · 2024Article
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- 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
- Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction.International journal of molecular sciences · 2022Article
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- LGCA-VHPPI: A local-global residue context aware viral-host protein-protein interaction predictor.PloS one · 2022Article
- Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions.Frontiers in microbiology · 2022Article
- Insights of Host Physiological Parameters and Gut Microbiome of Indian Type 2 Diabetic Patients VisualizedFrontiers in microbiology · 2022Article
- Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networks.Computational and structural biotechnology journal · 2022Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- A multitask transfer learning framework for the prediction of virus-human protein-protein interactions.BMC bioinformatics · 2021Article
- MitomiRs: their roles in mitochondria and importance in cancer cell metabolism.Radiology and oncology · 2021Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundViral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics.
methodsIn this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques.
resultsOut of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively.
conclusionThe proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.