ArticleTransboundary and emerging diseases2021
Computational predicting the human infectivity of H7N9 influenza viruses isolated from avian hosts.
Article in Transboundary and emerging diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Influenza virus genotype to phenotype predictions through machine learning: a systematic review.Emerging microbes & infections · 2021Pooled it
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Comparison of artificial intelligence image processing with manual leucocyte differential to score immune cell infiltration in a mouse infection model of cystic fibrosis.Journal of pathology informatics · 2025Article
- Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold.PLoS pathogens · 2024Article
- Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data.Communications biology · 2024Article
- RNA Sequencing Demonstrates That Circular RNA Regulates Avian Influenza Virus Replication in Human Cells.International journal of molecular sciences · 2022Article
- Computational predicting the human infectivity of H7N9 influenza viruses isolated from avian hosts.Transboundary and emerging diseases · 2021Article
- Author's Reply: Hazardous Postoperative Outcomes of Unexpected COVID-19 Infected Patients: A Call for Global Consideration of Sampling All Asymptomatic Patients Before Surgical Treatment.World journal of surgery · 2020Article
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Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
Abstract
The genome composition of a given avian influenza virus is the primary determinant of its potential for cross-species transmission from birds to humans. Here, we introduce a viral genome-based computational tool that can be used to evaluate the human infectivity of avian isolates of influenza A H7N9 viruses, which can enable prediction of the potential risk of these isolates infecting humans. This tool, which is based on a novel class weight-biased logistic regression (CWBLR) algorithm, uses the sequences of the eight genome segments of an H7N9 strain as the input and gives the probability of this strain infecting humans (reflecting its human infectivity). We examined the replication efficiency and the pathogenicity of several H7N9 avian isolates that were predicted to have very low or high human infectivity by the CWBLR model in cell culture and in mice, and found that the strains with high predicted human infectivity replicated more efficiently in mammalian cells and were more infective in mice than those that were predicted to have low human infectivity. These results demonstrate that our CWBLR model can serve as a powerful tool for predicting the human infectivity and cross-species transmission risks of H7N9 avian strains.
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