ArticleInternational journal of molecular sciences2017
Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest.
Article in International journal of molecular sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Influenza virus genotype to phenotype predictions through machine learning: a systematic review.Emerging microbes & infections · 2021Pooled it
- Zoon0PredV: Potential Virus Species Crossover Prediction Using Convolutional Neural Networks and Viral Protein Sequence Patterns.Bioinformatics and biology insights · 2026Article
- Predicting host tropism in influenza a viruses: insights from multi-segment nucleotide signatures.Journal of translational medicine · 2025Article
- Deep learning predicts potential reassortments of avian H5N1 with human influenza viruses.National science review · 2025Article
- Flu-CNN: identifying host specificity of Influenza A virus using convolutional networks.Human genomics · 2025Article
- WaveSeekerNet: accurate prediction of influenza A virus subtypes and host source using attention-based deep learning.GigaScience · 2025Article
- General Intelligence Framework to Predict Virus Adaptation Based on a Genome Language Model.Research (Washington, D.C.) · 2025Article
- Exploring genetic signatures of zoonotic influenza A virus at the swine-human interface with phylogenetic and ancestral sequence reconstruction.Virus evolution · 2025Article
- Mammalian adaptation risk in HPAI H5N8: a comprehensive model bridging experimental data with mathematical insights.Emerging microbes & infections · 2024Article
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- Managing the deluge of newly discovered plant viruses and viroids: an optimized scientific and regulatory framework for their characterization and risk analysis.Frontiers in microbiology · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Identifying host-specific amino acid signatures for influenza A viruses using an adjusted entropy measure.BMC bioinformatics · 2022Article
- The future of zoonotic risk prediction.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Avian Influenza in Wild Birds and Poultry: Dissemination Pathways, Monitoring Methods, and Virus Ecology.Pathogens (Basel, Switzerland) · 2021Review
- Machine Learning Methods for Predicting Human-Adaptive Influenza A Viruses Based on Viral Nucleotide Compositions.Molecular biology and evolution · 2020Article
- HopPER: an adaptive model for probability estimation of influenza reassortment through host prediction.BMC medical genomics · 2020Article
- Identifying genes associated with invasive disease in S. pneumoniae by applying a machine learning approach to whole genome sequence typing data.Scientific reports · 2019Article
- Molecular Research on Emerging Viruses: Evolution, Diagnostics, Pathogenesis, and Therapeutics.International journal of molecular sciences · 2018Article
- Natural Reassortants of Potentially Zoonotic Avian Influenza Viruses H5N1 and H9N2 from Egypt Display Distinct Pathogenic Phenotypes in Experimentally Infected Chickens and Ferrets.Journal of virology · 2017Article
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
Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak.
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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.