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Biol."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The evolution of severe acute respiratory syndrome coronavirus 2 (SARS\u2010CoV\u20102) during the coronavirus disease 2019 (COVID\u201019) pandemic highlights the critical need for predicting virus mutations in order to stay ahead of infectious diseases. Here, we present the time\u2010enhanced attention\u2010based model (TEAM), which combines phylogenetic sampling with deep learning to improve virus mutation prediction accuracy. TEAM introduces a novel time\u2010enhanced phylogenetic sampling strategy that preserves both evolutionary and temporal sequence relationships, enhancing its ability to predict site\u2010specific mutations as a multi\u2010class classification task. The framework leverages evolutionary scale modeling embeddings and a dual\u2010attention mechanism to capture sequence\u2010level patterns and temporal dynamics. Experiments on the SARS\u2010CoV\u20102 spike protein dataset show that TEAM substantially outperforms existing methods. Further evaluations on membrane protein and H1N1 datasets confirm its robustness and generalizability. In practice, TEAM provides a scalable and interpretable solution for mutation prediction, offering valuable insights for evolutionary research and public health planning.<\/jats:p>","DOI":"10.1002\/qub2.70049","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T04:05:46Z","timestamp":1786939546000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TEAM: A time\u2010enhanced attention\u2010based model for virus mutation prediction"],"prefix":"10.1002","volume":"14","author":[{"given":"Jie","family":"Ji","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science The Chinese University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0597-4176","authenticated-orcid":false,"given":"Jie","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mathematical Science Xiamen University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianwei","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Data Science The Chinese University of Hong Kong\u2010Shenzhen  Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2744-9030","authenticated-orcid":false,"given":"Xiaodan","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science The Chinese University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,16]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-76550-z"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.04.045"},{"key":"e_1_2_10_4_1","article-title":"COVID\u201019 image data collection: prospective predictions are the future","author":"Cohen JP","year":"2020","journal-title":"Preprint at arXiv: 2006.11988"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109853"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109850"},{"key":"e_1_2_10_7_1","article-title":"Non\u2010pharmaceutical interventions (NPIs) to reduce COVID\u201019 mortality","author":"Gandhi KR","year":"2020","journal-title":"SSRN Electron J"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa050"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106264"},{"key":"e_1_2_10_10_1","unstructured":"RumelhartDE HintonGE WilliamsRJ.Learning internal representations by error propagation. 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