{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li L"],"funding":["Chinese Academy of Sciences","Ministry of Science and Technology of the People's Republic of China","Ministry of Science and Technology of the People's Republic of China","National Natural Science Foundation of China","Alliance of National and International·Science Organizations for the Belt·and·Road Regions","National Key Research & Development Program of China","Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park"],"pagination":["e2405058"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11615786"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(45)"],"pubmed_abstract":["The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves over the past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention and control. However, detecting high-risk variants through experimental and epidemiological research is time-consuming and often lags behind the emergence and spread of these variants. In this study, HiRisk-Detector a machine learning algorithm based on haplotype network, is developed for computationally early detecting high-risk SARS-CoV-2 variants. Leveraging over 7.6 million high-quality and complete SARS-CoV-2 genomes and metadata, the effectiveness, robustness, and generalizability of HiRisk-Detector are validated. First, HiRisk-Detector is"],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants."],"pmcid":["PMC11615786"],"funding_grant_id":["32270718","32170678","Z211100002121006","ANSO-CR-KP-2022-09","2023YFC3041500","2023YFC2604400","XDB38030200","2021YFF0703703","Y2021038"],"pubmed_authors":["Li C","Zhang Z","Bao Y","Zhao W","Song S","Luo H","Zou D","Li L","Li N","Xue Y"],"additional_accession":[]},"is_claimable":false,"name":"Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.","description":"The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves over the past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention and control. However, detecting high-risk variants through experimental and epidemiological research is time-consuming and often lags behind the emergence and spread of these variants. In this study, HiRisk-Detector a machine learning algorithm based on haplotype network, is developed for computationally early detecting high-risk SARS-CoV-2 variants. Leveraging over 7.6 million high-quality and complete SARS-CoV-2 genomes and metadata, the effectiveness, robustness, and generalizability of HiRisk-Detector are validated. First, HiRisk-Detector is","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2026-06-03T00:35:50.672Z","creation":"2025-04-19T14:40:24.147Z"},"accession":"S-EPMC11615786","cross_references":{"pubmed":["39401400"],"doi":["10.1002/advs.202405058"]}}