Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Vaccinations01:51

Vaccinations

44.8K
Overview
44.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine Learning Identification of Cell-Type-Specific Molecular Signatures Distinguishing COVID-19 from Other Lower Respiratory Tract Diseases.

Life (Basel, Switzerland)·2026
Same author

Machine Learning-Based Identification of Candidate Serum miRNA Features for Pan-Cancer and Cancer Type Classification.

Life (Basel, Switzerland)·2026
Same author

Network Analysis in Microbiome Research: Methods, Tools, and Applications.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

The Role of EBV Infection and Epigenetic Factors in Radioresistance of Colorectal Cancer.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

RNA Large Language Models in Virology.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

Protein Language Models in Virology: A Review of Advances and Applications.

Methods in molecular biology (Clifton, N.J.)·2026

Related Experiment Video

Updated: Jul 25, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

2.9K

Machine Learning Classification of Time since BNT162b2 COVID-19 Vaccination Based on Array-Measured Antibody

Qing-Lan Ma1, Fei-Ming Huang1, Wei Guo2

  • 1School of Life Sciences, Shanghai University, Shanghai 200444, China.

Life (Basel, Switzerland)
|June 28, 2023
PubMed
Summary
This summary is machine-generated.

Immunity to SARS-CoV-2 wanes post-vaccination. This study identified key antibodies that change over time, offering insights to improve long-term vaccine effectiveness against COVID-19 variants.

Keywords:
COVID-19 vaccinationantigenmachine learning

More Related Videos

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes
08:52

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

Published on: July 26, 2019

8.2K
Author Spotlight: Advancing Immune Monitoring in Critical Care Patients Using Whole Blood Assays
06:03

Author Spotlight: Advancing Immune Monitoring in Critical Care Patients Using Whole Blood Assays

Published on: September 20, 2024

1.3K

Related Experiment Videos

Last Updated: Jul 25, 2025

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19
08:48

Dynamic Monitoring of Seroconversion using a Multianalyte Immunobead Assay for Covid-19

Published on: February 16, 2022

2.9K
Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes
08:52

Use of an Influenza Antigen Microarray to Measure the Breadth of Serum Antibodies Across Virus Subtypes

Published on: July 26, 2019

8.2K
Author Spotlight: Advancing Immune Monitoring in Critical Care Patients Using Whole Blood Assays
06:03

Author Spotlight: Advancing Immune Monitoring in Critical Care Patients Using Whole Blood Assays

Published on: September 20, 2024

1.3K

Area of Science:

  • Immunology
  • Computational Biology
  • Virology

Background:

  • SARS-CoV-2 immunity diminishes over time after vaccination.
  • Understanding antibody dynamics post-vaccination is crucial for enhancing vaccine efficacy.
  • Prevalent SARS-CoV-2 variants during the study period included B.1.1.7, B.1.351, and P.1.

Purpose of the Study:

  • To identify key changes in antigen-reactive antibodies over time after COVID-19 vaccination.
  • To develop a machine learning framework for selecting essential antibodies against SARS-CoV-2 antigens.
  • To understand the role of specific antibodies in maintaining long-term immunity.

Main Methods:

  • Reanalysis of blood antibody level data from vaccinated and unvaccinated healthcare workers.
  • Utilized a machine learning framework with feature selection and classification algorithms.
  • Employed an antigen microarray featuring ten distinct SARS-CoV-2 antigens (nucleocapsid and spike proteins).

Main Results:

  • Identified several highly ranked antibody features, including those targeting Spike protein subunits (S1, S2) and receptor-binding domains.
  • Constructed efficient classifiers with a weighted F1 value around 0.75.
  • Obtained classification rules from an optimal decision tree to quantify antigen roles.

Conclusions:

  • Identified antibodies associated with decreased clinical immunity based on time post-vaccination.
  • These findings have significant implications for developing strategies to maintain long-term SARS-CoV-2 immunity.
  • The study highlights the importance of monitoring antibody responses to optimize vaccine performance.