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Vaccinations

Vaccinations

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Related Experiment Video

Updated: Jul 8, 2025

Author Spotlight: Magnetic Fluorescent Bead-Based Dual-Reporter Flow Analysis of PDL1-Vaxx Peptide Vaccine-Induced Antibody Blockade of the PD-1/PD-L1 Interaction

Author Spotlight: Magnetic Fluorescent Bead-Based Dual-Reporter Flow Analysis of PDL1-Vaxx Peptide Vaccine-Induced Antibody Blockade of the PD-1/PD-L1 Interaction

Published on: July 7, 2023

Vaxign-DL: A Deep Learning-based Method for Vaccine Design and its Evaluation.

Yuhan Zhang¹, Anthony Huffman², Justin Johnson¹

¹Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.

Biorxiv : the Preprint Server for Biology

|December 11, 2023

Summary

We developed Vaxign-DL, a deep learning tool for identifying bacterial vaccine candidates. This computational approach achieved high accuracy, outperforming previous methods in predicting protective antigens.

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Last Updated: Jul 8, 2025

Author Spotlight: Magnetic Fluorescent Bead-Based Dual-Reporter Flow Analysis of PDL1-Vaxx Peptide Vaccine-Induced Antibody Blockade of the PD-1/PD-L1 Interaction

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Methodology for the Efficient Generation of Fluorescently Tagged Vaccinia Virus Proteins

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Published on: January 17, 2014

Area of Science:

Computational biology
Vaccine development
Bioinformatics

Background:

Reverse vaccinology (RV) systematically identifies vaccine candidates from protein sequences.
Machine learning (ML) enhances prediction accuracy in RV.
Previous work established Vaxign-ML using eXtreme Gradient Boosting (XGBoost).

Conclusions:

Deep learning offers a powerful, data-driven approach for computational vaccine design.
Vaxign-DL represents a significant advancement in identifying effective bacterial vaccine candidates.
The study highlights the potential of DL in accelerating vaccine development pipelines.