ArticleNational science review2025
Deep learning predicts potential reassortments of avian H5N1 with human influenza viruses.
Article in National science review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Integrating prior knowledge inference with computational multi-omics analysis to reveal host antiviral networks of natural compounds against influenza A virus.Frontiers in cellular and infection microbiology · 2026Article
- Genomic similarity to quantitatively evaluate the reassortment potential of H7N9 with other subtypes of avian influenza viruses.Frontiers in cellular and infection microbiology · 2026Article
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Frequent infection cases with avian H5N1 influenza A viruses (IAVs) are posing pandemic risks of human/avian-reassorted IAVs. We aimed to build an attentional deep learning model named HAIRANGE, for predicting potentially human-adapted reassortment of H5N1 and human IAVs. A biologically relevant and non-pretrained embedder named Codon2Vec in HAIRANGE performed competitively in benchmarking against other embedders, such as ESM2, DNABERT2 and others, indicating a high association of genomic context with viral hosts or serotypes, for IAV RNA polymerase-related genes. HAIRANGE accurately predicted the adaptation of each polymerase-related gene and the adaptive polymerase-related gene reassortment with polymerase activity validated by
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Registered trials
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