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Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS
The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337631/ https://www.ncbi.nlm.nih.gov/pubmed/35905092 http://dx.doi.org/10.1371/journal.pone.0263954 |
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author | Rashidi, Hooman H. Pepper, John Howard, Taylor Klein, Karina May, Larissa Albahra, Samer Phinney, Brett Salemi, Michelle R. Tran, Nam K. |
author_facet | Rashidi, Hooman H. Pepper, John Howard, Taylor Klein, Karina May, Larissa Albahra, Samer Phinney, Brett Salemi, Michelle R. Tran, Nam K. |
author_sort | Rashidi, Hooman H. |
collection | PubMed |
description | The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method’s robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset. |
format | Online Article Text |
id | pubmed-9337631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93376312022-07-30 Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS Rashidi, Hooman H. Pepper, John Howard, Taylor Klein, Karina May, Larissa Albahra, Samer Phinney, Brett Salemi, Michelle R. Tran, Nam K. PLoS One Research Article The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method’s robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset. Public Library of Science 2022-07-29 /pmc/articles/PMC9337631/ /pubmed/35905092 http://dx.doi.org/10.1371/journal.pone.0263954 Text en © 2022 Rashidi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rashidi, Hooman H. Pepper, John Howard, Taylor Klein, Karina May, Larissa Albahra, Samer Phinney, Brett Salemi, Michelle R. Tran, Nam K. Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS |
title | Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS |
title_full | Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS |
title_fullStr | Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS |
title_full_unstemmed | Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS |
title_short | Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS |
title_sort | comparative performance of two automated machine learning platforms for covid-19 detection by maldi-tof-ms |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9337631/ https://www.ncbi.nlm.nih.gov/pubmed/35905092 http://dx.doi.org/10.1371/journal.pone.0263954 |
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