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Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response
The immune system acts as an intricate apparatus that is dedicated to mounting a defense and ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are a critical tool to be developed. However, vaccine res...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384938/ https://www.ncbi.nlm.nih.gov/pubmed/37515051 http://dx.doi.org/10.3390/vaccines11071236 |
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author | Affaticati, Fabio Bartholomeus, Esther Mullan, Kerry Damme, Pierre Van Beutels, Philippe Ogunjimi, Benson Laukens, Kris Meysman, Pieter |
author_facet | Affaticati, Fabio Bartholomeus, Esther Mullan, Kerry Damme, Pierre Van Beutels, Philippe Ogunjimi, Benson Laukens, Kris Meysman, Pieter |
author_sort | Affaticati, Fabio |
collection | PubMed |
description | The immune system acts as an intricate apparatus that is dedicated to mounting a defense and ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are a critical tool to be developed. However, vaccine responses are governed by levels that, when interrogated, separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modeling could aid in gaining actionable insights on response markers shared across populations, capture the immune system’s diversity, and disentangle confounders. We thus sought to assess this multi-view modeling capacity on the responsiveness to the Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine-induced antibodies against the HBV surface antigen (anti-HBs) in early converters (n = 21; <2 months) and late converters (n = 9; <6 months) and was defined based on the anti-HBs titers (>10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T-cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modeling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction outperformed single-view methods in terms of the area under the curve and balanced accuracy, confirming the increase in predictive power to be gained. The interpretation of these findings showed that age, gender, inflammation-related gene sets, and pre-existing vaccine-specific T-cells could be associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements clinical seroconversion and all single modalities. Importantly, this modeling could identify what features could predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify the key features regulating responsiveness. |
format | Online Article Text |
id | pubmed-10384938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103849382023-07-30 Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response Affaticati, Fabio Bartholomeus, Esther Mullan, Kerry Damme, Pierre Van Beutels, Philippe Ogunjimi, Benson Laukens, Kris Meysman, Pieter Vaccines (Basel) Article The immune system acts as an intricate apparatus that is dedicated to mounting a defense and ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are a critical tool to be developed. However, vaccine responses are governed by levels that, when interrogated, separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modeling could aid in gaining actionable insights on response markers shared across populations, capture the immune system’s diversity, and disentangle confounders. We thus sought to assess this multi-view modeling capacity on the responsiveness to the Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine-induced antibodies against the HBV surface antigen (anti-HBs) in early converters (n = 21; <2 months) and late converters (n = 9; <6 months) and was defined based on the anti-HBs titers (>10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T-cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modeling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction outperformed single-view methods in terms of the area under the curve and balanced accuracy, confirming the increase in predictive power to be gained. The interpretation of these findings showed that age, gender, inflammation-related gene sets, and pre-existing vaccine-specific T-cells could be associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements clinical seroconversion and all single modalities. Importantly, this modeling could identify what features could predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify the key features regulating responsiveness. MDPI 2023-07-13 /pmc/articles/PMC10384938/ /pubmed/37515051 http://dx.doi.org/10.3390/vaccines11071236 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Affaticati, Fabio Bartholomeus, Esther Mullan, Kerry Damme, Pierre Van Beutels, Philippe Ogunjimi, Benson Laukens, Kris Meysman, Pieter Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response |
title | Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response |
title_full | Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response |
title_fullStr | Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response |
title_full_unstemmed | Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response |
title_short | Multi-View Learning to Unravel the Different Levels Underlying Hepatitis B Vaccine Response |
title_sort | multi-view learning to unravel the different levels underlying hepatitis b vaccine response |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10384938/ https://www.ncbi.nlm.nih.gov/pubmed/37515051 http://dx.doi.org/10.3390/vaccines11071236 |
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