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Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dec, 2024;11 (45): e2405058.

Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.

Lun Li, Cuiping Li, Na Li, Dong Zou, Wenming Zhao, Hong Luo, Yongbiao Xue, Zhang Zhang, Yiming Bao, Shuhui Song
Author Information
  1. Lun Li: China National Center for Bioinformation, Beijing, 100101, China. ORCID
  2. Cuiping Li: China National Center for Bioinformation, Beijing, 100101, China.
  3. Na Li: China National Center for Bioinformation, Beijing, 100101, China.
  4. Dong Zou: China National Center for Bioinformation, Beijing, 100101, China.
  5. Wenming Zhao: China National Center for Bioinformation, Beijing, 100101, China.
  6. Hong Luo: China National Center for Bioinformation, Beijing, 100101, China.
  7. Yongbiao Xue: China National Center for Bioinformation, Beijing, 100101, China.
  8. Zhang Zhang: China National Center for Bioinformation, Beijing, 100101, China.
  9. Yiming Bao: China National Center for Bioinformation, Beijing, 100101, China.
  10. Shuhui Song: China National Center for Bioinformation, Beijing, 100101, China. ORCID
PMID: 39401400 DOI: 10.1002/advs.202405058

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 evaluated on actual empirical data, successfully detecting all 13 high-risk variants, preceding World Health Organization announcements by 27 days on average. Second, its robustness is tested by reducing sequencing intensity to one-fourth, noting only a minimal delay of 3.8 days, demonstrating its effectiveness. Third, HiRisk-Detector is applied to detect risks among SARS-CoV-2 Omicron variant sub-lineages, confirming its broad applicability and high ROC-AUC and PR-AUC performance. Overall, HiRisk-Detector features powerful capacity for early detection of high-risk variants, bearing great utility for any public emergency caused by infectious diseases or viruses.

Keywords

SARS‐CoV‐2 haplotype network high‐risk variant machine learning pre‐warning

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Grants

  1. 2023YFC3041500/Ministry of Science and Technology of the People's Republic of China
  2. ANSO-CR-KP-2022-09/Alliance of National and International·Science Organizations for the Belt·and·Road Regions
  3. Z211100002121006/Beijing Municipal Science & Technology Commission, Administrative Commission of Zhongguancun Science Park
  4. 2021YFF0703703/National Key Research & Development Program of China
  5. 2023YFC2604400/National Key Research & Development Program of China
  6. XDB38030200/Chinese Academy of Sciences
  7. Y2021038/Chinese Academy of Sciences
  8. 32170678/National Natural Science Foundation of China
  9. 32270718/National Natural Science Foundation of China

MeSH Term

SARS-CoV-2
Humans
COVID-19
Machine Learning
Genome, Viral
Algorithms
Journal Article

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