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Statistical and machine learning methods for immunoprofiling based on single-cell data

Immunoprofiling has become a crucial tool for understanding the complex interactions between the immune system and diseases or interventions, such as therapies and vaccinations. Immune response biomarkers are critical for understanding those relationships and potentially developing personalized inte...

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Detalles Bibliográficos
Autores principales: Zhang, Jingxuan, Li, Jia, Lin, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373621/
https://www.ncbi.nlm.nih.gov/pubmed/37485833
http://dx.doi.org/10.1080/21645515.2023.2234792
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author Zhang, Jingxuan
Li, Jia
Lin, Lin
author_facet Zhang, Jingxuan
Li, Jia
Lin, Lin
author_sort Zhang, Jingxuan
collection PubMed
description Immunoprofiling has become a crucial tool for understanding the complex interactions between the immune system and diseases or interventions, such as therapies and vaccinations. Immune response biomarkers are critical for understanding those relationships and potentially developing personalized intervention strategies. Single-cell data have emerged as a promising source for identifying immune response biomarkers. In this review, we discuss the current state-of-the-art methods for immunoprofiling, including those for reducing the dimensionality of high-dimensional single-cell data and methods for clustering, classification, and prediction. We also draw attention to recent developments in data integration.
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spelling pubmed-103736212023-07-28 Statistical and machine learning methods for immunoprofiling based on single-cell data Zhang, Jingxuan Li, Jia Lin, Lin Hum Vaccin Immunother Technology Immunoprofiling has become a crucial tool for understanding the complex interactions between the immune system and diseases or interventions, such as therapies and vaccinations. Immune response biomarkers are critical for understanding those relationships and potentially developing personalized intervention strategies. Single-cell data have emerged as a promising source for identifying immune response biomarkers. In this review, we discuss the current state-of-the-art methods for immunoprofiling, including those for reducing the dimensionality of high-dimensional single-cell data and methods for clustering, classification, and prediction. We also draw attention to recent developments in data integration. Taylor & Francis 2023-07-24 /pmc/articles/PMC10373621/ /pubmed/37485833 http://dx.doi.org/10.1080/21645515.2023.2234792 Text en © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
spellingShingle Technology
Zhang, Jingxuan
Li, Jia
Lin, Lin
Statistical and machine learning methods for immunoprofiling based on single-cell data
title Statistical and machine learning methods for immunoprofiling based on single-cell data
title_full Statistical and machine learning methods for immunoprofiling based on single-cell data
title_fullStr Statistical and machine learning methods for immunoprofiling based on single-cell data
title_full_unstemmed Statistical and machine learning methods for immunoprofiling based on single-cell data
title_short Statistical and machine learning methods for immunoprofiling based on single-cell data
title_sort statistical and machine learning methods for immunoprofiling based on single-cell data
topic Technology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10373621/
https://www.ncbi.nlm.nih.gov/pubmed/37485833
http://dx.doi.org/10.1080/21645515.2023.2234792
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