Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of p...

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Bibliographic Details
Published in:npj vaccines Vol. 9; no. 1; pp. 15 - 14
Main Author: Bravi, Barbara
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 20.01.2024
Nature Publishing Group
Nature Portfolio
Subjects:
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Biomedical and Life Sciences
Biomedicine
Design
Immunology
Infectious Diseases
Machine learning
Medical Microbiology
Medical research
Perspective
Public Health
Vaccine
Vaccines
Virology
ISSN:2059-0105, 2059-0105
Online Access:Get full text
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Summary:Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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ISSN:2059-0105
2059-0105
DOI:10.1038/s41541-023-00795-8