ArticleBiomedical journal2020
Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.
Article in Biomedical journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.
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Who cites it
49 citing papers in PubMed, 1 synthesis or guideline pooled it, 124 citations in OpenAlex.
- Phytochemicals against SARS-CoV as potential drug leads.Biomedical journal · 2021Pooled it
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- 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
- ClinIAN: Clinically Informed Attention Network.Bioinformatics advances · 2026Article
- Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response.Bioinformatics advances · 2026Article
- Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management.Veterinary sciences · 2025Review
- Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions.Computational and structural biotechnology journal · 2025Article
- A graph neural network-based approach for predicting SARS-CoV-2-human protein interactions from multiview data.PloS one · 2025Article
- Article
- An optimized deep-forest algorithm using a modified differential evolution optimization algorithm: A case of host-pathogen protein-protein interaction prediction.Computational and structural biotechnology journal · 2025Article
- Statistical analysis of sequential motifs at biologically relevant protein-protein interfaces.Computational and structural biotechnology journal · 2024Article
- Prediction models for COVID-19 disease outcomes.Emerging microbes & infections · 2024Article
- HBFormer: a single-stream framework based on hybrid attention mechanism for identification of human-virus protein-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.Briefings in bioinformatics · 2024Article
- Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology.BMC bioinformatics · 2024Article
- Significance of Sequence Features in Classification of Protein-Protein Interactions Using Machine Learning.The protein journal · 2024Article
- Host factors of SARS-CoV-2 in infection, pathogenesis, and long-term effects.Frontiers in cellular and infection microbiology · 2024Review
- Assessing the Potential Contribution of In Silico Studies in Discovering Drug Candidates That Interact with Various SARS-CoV-2 Receptors.International journal of molecular sciences · 2023Review
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified.
methodIn this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad.
resultWe have built an ensemble voting classifier using SVM
conclusionThis study may encourage the identification of potential targets for more effective anti-COVID drug discovery.
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Registered trials
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.