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@ARTICLE{Zhai:291119,
      author       = {K. Zhai and J. Dong and J. Zeng and P. Cheng and X. Wu and
                      W. Han and Y. Chen and Z. Qiu$^*$ and Y. Zhou and J. Pu and
                      T. Jiang and X. Du},
      title        = {{G}lobal {A}ntigenic {L}andscape and {V}accine
                      {R}ecommendation {S}trategy for {L}ow {P}athogenic {A}vian
                      {I}nfluenza {A}({H}9{N}2) {V}iruses.},
      journal      = {Journal of infection},
      volume       = {89},
      number       = {2},
      issn         = {0163-4453},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {DKFZ-2024-01322},
      pages        = {106199},
      year         = {2024},
      note         = {2024 Jun 18;89(2):106199},
      abstract     = {The sustained circulating of H9N2 avian influenza viruses
                      (AIVs) poses a significant threat for contributing to a new
                      pandemic. Given the temporal and spatial uncertainty in
                      antigenicity of H9N2 AIVs, the immune protection efficiency
                      of vaccines remains challenging. By developing an
                      antigenicity prediction method for H9N2 AIVs, named
                      PREDAC-H9, the global antigenic landscape of H9N2 AIVs was
                      mapped. PREDAC-H9 utilizes the XGBoost model with 14
                      well-designed features. The XGBoost model was built and
                      evaluated to predict the antigenic relationship between any
                      two viruses with high values of $81.1\%,$ $81.4\%,$
                      $81.3\%,$ $81.1\%,$ and $89.4\%$ in accuracy, precision,
                      recall, F1 value, and area under curve (AUC), respectively.
                      Then the antigenic correlation network (ACnet) was
                      constructed based on the predicted antigenic relationship
                      for H9N2 AIVs from 1966 to 2022, and ten major antigenic
                      clusters were identified. Of these, four novel clusters were
                      generated in China in the past decade, demonstrating the
                      unique complex situation there. To help tackle this
                      situation, we applied PREDAC-H9 to calculate the
                      cluster-transition determining sites and screen out virus
                      strains with high cross-protective spectrum, thus providing
                      in-silico reference for vaccine recommendation. The proposed
                      model will reduce the clinical monitoring workload and
                      provide useful tool for surveillance and control of H9N2
                      AIVs. AVAILABILITY OF DATA AND MATERIALS: The data that
                      support the findings of this study are available in the
                      Supplementary Data.},
      keywords     = {H9N2 (Other) / antigenic cluster (Other) / avian influenza
                      (Other) / surveillance (Other) / vaccine recommendation
                      (Other)},
      cin          = {E055},
      ddc          = {610},
      cid          = {I:(DE-He78)E055-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38901571},
      doi          = {10.1016/j.jinf.2024.106199},
      url          = {https://inrepo02.dkfz.de/record/291119},
}