InfluProto: Predicting Cross-Species Transmission of Influenza A Virus via Prototype Learning
- 1Beijing Institute of Genomics, Chinese Academy of Sciences
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.
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BibTeX
@misc{geng2026influproto,
title = {{InfluProto: Predicting Cross-Species Transmission of Influenza A Virus via Prototype Learning}},
author = {Z. Geng and L. Li and R. Ye and Y. Wang and S. Song},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.07.757480},
url = {https://www.biorxiv.org/content/10.64898/2026.10.07.757480v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - InfluProto: Predicting Cross-Species Transmission of Influenza A Virus via Prototype Learning
AU - Z. Geng
AU - L. Li
AU - R. Ye
AU - Y. Wang
AU - S. Song
PY - 2026
DA - 2026/10/07
PB - bioRxiv
DO - 10.64898/2026.10.07.757480
UR - https://www.biorxiv.org/content/10.64898/2026.10.07.757480v1
AB - 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.
ER -