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Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.
In: PLoS ONE, Jg. 17 (2022-07-29), Heft 7, S. 1-11
Online
academicJournal
Zugriff:
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. [ABSTRACT FROM AUTHOR]
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Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.
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Autor/in / Beteiligte Person: | Rashidi, Hooman H. ; Pepper, John ; Howard, Taylor ; Klein, Karina ; May, Larissa ; Albahra, Samer ; Phinney, Brett ; Salemi, Michelle R. ; Tran, Nam K. |
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Zeitschrift: | PLoS ONE, Jg. 17 (2022-07-29), Heft 7, S. 1-11 |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 1932-6203 (print) |
DOI: | 10.1371/journal.pone.0263954 |
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