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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2023
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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. |
format | Online Article Text |
id | pubmed-10373621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhangjingxuan statisticalandmachinelearningmethodsforimmunoprofilingbasedonsinglecelldata AT lijia statisticalandmachinelearningmethodsforimmunoprofilingbasedonsinglecelldata AT linlin statisticalandmachinelearningmethodsforimmunoprofilingbasedonsinglecelldata |