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Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning
Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased m...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659538/ https://www.ncbi.nlm.nih.gov/pubmed/33205062 http://dx.doi.org/10.1016/j.xcrm.2020.100053 |
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author | Acquaviva, Massimo Menon, Ramesh Di Dario, Marco Dalla Costa, Gloria Romeo, Marzia Sangalli, Francesca Colombo, Bruno Moiola, Lucia Martinelli, Vittorio Comi, Giancarlo Farina, Cinthia |
author_facet | Acquaviva, Massimo Menon, Ramesh Di Dario, Marco Dalla Costa, Gloria Romeo, Marzia Sangalli, Francesca Colombo, Bruno Moiola, Lucia Martinelli, Vittorio Comi, Giancarlo Farina, Cinthia |
author_sort | Acquaviva, Massimo |
collection | PubMed |
description | Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS. |
format | Online Article Text |
id | pubmed-7659538 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76595382020-11-16 Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning Acquaviva, Massimo Menon, Ramesh Di Dario, Marco Dalla Costa, Gloria Romeo, Marzia Sangalli, Francesca Colombo, Bruno Moiola, Lucia Martinelli, Vittorio Comi, Giancarlo Farina, Cinthia Cell Rep Med Article Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS. Elsevier 2020-07-21 /pmc/articles/PMC7659538/ /pubmed/33205062 http://dx.doi.org/10.1016/j.xcrm.2020.100053 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Acquaviva, Massimo Menon, Ramesh Di Dario, Marco Dalla Costa, Gloria Romeo, Marzia Sangalli, Francesca Colombo, Bruno Moiola, Lucia Martinelli, Vittorio Comi, Giancarlo Farina, Cinthia Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning |
title | Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning |
title_full | Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning |
title_fullStr | Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning |
title_full_unstemmed | Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning |
title_short | Inferring Multiple Sclerosis Stages from the Blood Transcriptome via Machine Learning |
title_sort | inferring multiple sclerosis stages from the blood transcriptome via machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7659538/ https://www.ncbi.nlm.nih.gov/pubmed/33205062 http://dx.doi.org/10.1016/j.xcrm.2020.100053 |
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