Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets

Abstract COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including prot...

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Autores principales:	Georgios Papoutsoglou, Makrina Karaglani, Vincenzo Lagani, Naomi Thomson, Oluf Dimitri Røe, Ioannis Tsamardinos, Ekaterini Chatzaki
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/760ad289441a49f7b30b75092e2b03be
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spelling	oai:doaj.org-article:760ad289441a49f7b30b75092e2b03be2021-12-02T17:55:03ZAutomated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets10.1038/s41598-021-94501-02045-2322https://doaj.org/article/760ad289441a49f7b30b75092e2b03be2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94501-0https://doaj.org/toc/2045-2322Abstract COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723–0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865–0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899–0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.Georgios PapoutsoglouMakrina KaraglaniVincenzo LaganiNaomi ThomsonOluf Dimitri RøeIoannis TsamardinosEkaterini ChatzakiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Georgios Papoutsoglou Makrina Karaglani Vincenzo Lagani Naomi Thomson Oluf Dimitri Røe Ioannis Tsamardinos Ekaterini Chatzaki Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
description	Abstract COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723–0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865–0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899–0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management.
format	article
author	Georgios Papoutsoglou Makrina Karaglani Vincenzo Lagani Naomi Thomson Oluf Dimitri Røe Ioannis Tsamardinos Ekaterini Chatzaki
author_facet	Georgios Papoutsoglou Makrina Karaglani Vincenzo Lagani Naomi Thomson Oluf Dimitri Røe Ioannis Tsamardinos Ekaterini Chatzaki
author_sort	Georgios Papoutsoglou
title	Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
title_short	Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
title_full	Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
title_fullStr	Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
title_full_unstemmed	Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets
title_sort	automated machine learning optimizes and accelerates predictive modeling from covid-19 high throughput datasets
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/760ad289441a49f7b30b75092e2b03be
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Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets

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