{
  "abstract": "Zoonotic influenza A viruses pose constitute a significant persistent threat to global public health, yet quantitative approaches for assessing their cross-species transmission potential remain limited. Here, we present InfluProto, a deep learning model that introduces prototype learning into host prediction for influenza A virus, framing conceptualizing host adaptation as a continuous process rather than a discrete outcome. InfluProto embeds viral sequences into a continuous representation space anchored by human, swine, and avian prototypes, using cosine distances as a quantitative measure of host adaptation. InfluProto accurately discriminated among the three host classes, achieving recall and precision exceeding 0.97, and substantially outperformed conventional classification models models--particularly on challenging zoonotic strains, including the 2009 pandemic H1N1 (pdm09) lineage. Beyond host classification, the learned representations captured biologically meaningful host-adaptive signatures,; notably, with pdm09 viruses occupying occupied an intermediate position between human and swine clusters, consistent with their evolutionary origin. Analysis of simulated reassortment further revealed that the introduction of human-origin segments into swine influenza viruses reduced their distance to the human prototype, indicating that InfluProto can detect reassortment-associated shifts in host adaptation. To facilitate broad public useaccess, we have developed an interactive web service for InfluProto. Collectively, our work establishes a continuous framework for characterizing host adaptation and evaluating zoonotic risk in influenza A viruses, with potential implications for pandemic surveillance and preparedness.",
  "abstract_shown": true,
  "affiliations": [
    "Beijing Institute of Genomics, Chinese Academy of Sciences"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Z. Geng"
    },
    {
      "name": "L. Li"
    },
    {
      "name": "R. Ye"
    },
    {
      "name": "Y. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Song"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/050gdsem9rpdw.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.07.757480",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757480v1.full",
  "id": "050gdsem9rpdw",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757480v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "microbiology"
    ],
    [
      "openalex",
      "2713|Influenza Virus Research Studies"
    ]
  ],
  "openalex": "W7220899544",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757480v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/050gdsem9rpdw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:40Z",
      "native_id": "10.64898/2026.10.07.757480",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.07.757480v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:microbiology",
      "subfield": "microbiology"
    }
  ],
  "title": "InfluProto: Predicting Cross-Species Transmission of Influenza A Virus via Prototype Learning",
  "title_text": "InfluProto: Predicting Cross-Species Transmission of Influenza A Virus via Prototype Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:14Z",
  "url": "https://research.draconitic.com/paper/050gdsem9rpdw",
  "venue": "bioRxiv",
  "versions": []
}