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Related Concept Videos

Influenza01:27

Influenza

Influenza is an acute, highly communicable viral disease that affects the respiratory tract and is responsible for seasonal epidemics worldwide. Influenza A is the most prevalent type associated with widespread outbreaks and is subtyped based on two surface glycoproteins: hemagglutinin (H) and neuraminidase (N), as in H1N1. These glycoproteins are essential for viral infectivity, transmission, and immune recognition. Transmission occurs primarily through respiratory droplets and contaminated...

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Related Experiment Video

Updated: Jun 8, 2026

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

Published on: July 26, 2019

A computational framework for influenza antigenic cartography.

Zhipeng Cai1, Tong Zhang, Xiu-Feng Wan

  • 1Department of Basic Sciences, College of Veterinary Medicine, Mississippi State University, Mississippi State, Mississippi, USA.

Plos Computational Biology
|October 16, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces Matrix Completion-Multidimensional Scaling (MC-MDS) to create influenza antigenic maps from incomplete hemagglutination inhibition (HI) data. This method aids in identifying influenza variants for better vaccine strain selection.

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Area of Science:

  • Virology
  • Computational Biology
  • Immunology

Background:

  • Influenza viruses pose a significant global public health threat.
  • Hemagglutination inhibition (HI) assays are crucial for influenza vaccine strain selection but provide limited antigenic characterization.
  • Combining multiple HI datasets results in incomplete matrices, hindering comprehensive antigenic analysis.

Purpose of the Study:

  • To develop a computational framework for constructing influenza antigenic cartography from incomplete HI data.
  • To improve the accuracy of antigenic characterization for influenza viruses.
  • To facilitate more effective influenza vaccine strain selection.

Main Methods:

  • Proposed a novel computational framework: Matrix Completion-Multidimensional Scaling (MC-MDS).
  • Employed low-rank matrix completion to reconstruct incomplete HI matrices.
  • Utilized multidimensional scaling to generate two-dimensional antigenic cartography.
  • Incorporated a temporal model to mitigate herd immunity bias in human influenza HI tables.

Main Results:

  • Successfully reconstructed HI matrices and generated antigenic cartography.
  • Identified eleven distinct clusters of antigenic variants for H3N2 influenza A viruses (1968-2003).
  • Demonstrated the utility of MC-MDS in identifying antigenic variants and major antigenic drift events over 36 years.

Conclusions:

  • The MC-MDS framework effectively addresses incomplete HI data for antigenic characterization.
  • The generated antigenic cartography aids in identifying influenza variants, supporting vaccine strain selection.
  • The computational approach offers a valuable tool for understanding influenza evolution and public health strategies.