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Related Experiment Video

Updated: Jul 17, 2025

Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing
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Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

Published on: March 15, 2019

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Using interpretable machine learning to extend heterogeneous antibody-virus datasets.

Tal Einav1,2, Rong Ma3

  • 1Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.

Cell Reports Methods
|September 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a computational framework to predict how antibodies inhibit influenza virus variants across different studies. This advance aids in unifying datasets and enhances pandemic preparedness by enabling broader data analysis.

Keywords:
antibody-virus interactionserror estimationhemagglutination inhibitionimputationinfluenzamatrix completionserology

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Area of Science:

  • Virology
  • Immunology
  • Computational Biology

Background:

  • Predicting experimental outcomes from existing biological data is a significant challenge.
  • Influenza virus evolution and data fragmentation across studies hinder pattern recognition and dataset unification.

Purpose of the Study:

  • To develop a computational framework for predicting antibody or serum inhibition of any influenza virus variant, regardless of the study.
  • To enable the unification of disparate influenza virus datasets and improve predictive capabilities.

Main Methods:

  • Developed a computational framework to predict cross-study antibody-antigen interactions.
  • Validated the framework using hemagglutination inhibition data from seven independent studies.
  • Predicted over 2,000,000 new inhibition values with associated uncertainties.

Main Results:

  • Quantified the transferability of data between human and ferret vaccination and infection studies.
  • Demonstrated a negative correlation between serum potency and breadth.
  • Generated a predictive tool applicable to diverse influenza studies.

Conclusions:

  • The computational framework successfully predicts antibody-antigen interactions across different studies, overcoming data fragmentation.
  • The findings offer insights into serum properties and provide a valuable tool for influenza pandemic preparedness.
  • This approach facilitates a more unified and comprehensive analysis of influenza virus data.