%0 Journal Article
%A Zhai, Ke
%A Dong, Jinze
%A Zeng, Jinfeng
%A Cheng, Peiwen
%A Wu, Xinsheng
%A Han, Wenjie
%A Chen, Yilin
%A Qiu, Zekai
%A Zhou, Yong
%A Pu, Juan
%A Jiang, Taijiao
%A Du, Xiangjun
%T Global Antigenic Landscape and Vaccine Recommendation Strategy for Low Pathogenic Avian Influenza A(H9N2) Viruses.
%J Journal of infection
%V 89
%N 2
%@ 0163-4453
%C Amsterdam [u.a.]
%I Elsevier
%M DKFZ-2024-01322
%P 106199
%D 2024
%Z 2024 Jun 18;89(2):106199
%X 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
%K H9N2 (Other)
%K antigenic cluster (Other)
%K avian influenza (Other)
%K surveillance (Other)
%K vaccine recommendation (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:38901571
%R 10.1016/j.jinf.2024.106199
%U https://inrepo02.dkfz.de/record/291119