Early prognosis of respiratory virus shedding in humans

Abstract This paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infect...

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Autores principales:	M. Aminian, T. Ghosh, A. Peterson, A. L. Rasmussen, S. Stiverson, K. Sharma, M. Kirby
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e5b962cbefe3457382315593b5185fa6
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spelling	oai:doaj.org-article:e5b962cbefe3457382315593b5185fa62021-12-02T16:34:54ZEarly prognosis of respiratory virus shedding in humans10.1038/s41598-021-95293-z2045-2322https://doaj.org/article/e5b962cbefe3457382315593b5185fa62021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95293-zhttps://doaj.org/toc/2045-2322Abstract This paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infected with HRV, RSV, H1N1, and H3N2. We address the problem of identifying discriminatory biomarkers between controls and eventual shedders in the first 32 h post-infection. Our exploratory analysis shows that the most discriminatory biomarkers exhibit a strong dependence on time over the course of the human response to infection. We visualize the feature sets to provide evidence of the rapid evolution of the gene expression profiles. To quantify this observation, we partition the data in the first 32 h into four equal time windows of 8 h each and identify all discriminatory biomarkers using sparsity-promoting classifiers and Iterated Feature Removal. We then perform a comparative machine learning classification analysis using linear support vector machines, artificial neural networks and Centroid-Encoder. We present a range of experiments on different groupings of the diseases to demonstrate the robustness of the resulting models.M. AminianT. GhoshA. PetersonA. L. RasmussenS. StiversonK. SharmaM. KirbyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M. Aminian T. Ghosh A. Peterson A. L. Rasmussen S. Stiverson K. Sharma M. Kirby Early prognosis of respiratory virus shedding in humans
description	Abstract This paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infected with HRV, RSV, H1N1, and H3N2. We address the problem of identifying discriminatory biomarkers between controls and eventual shedders in the first 32 h post-infection. Our exploratory analysis shows that the most discriminatory biomarkers exhibit a strong dependence on time over the course of the human response to infection. We visualize the feature sets to provide evidence of the rapid evolution of the gene expression profiles. To quantify this observation, we partition the data in the first 32 h into four equal time windows of 8 h each and identify all discriminatory biomarkers using sparsity-promoting classifiers and Iterated Feature Removal. We then perform a comparative machine learning classification analysis using linear support vector machines, artificial neural networks and Centroid-Encoder. We present a range of experiments on different groupings of the diseases to demonstrate the robustness of the resulting models.
format	article
author	M. Aminian T. Ghosh A. Peterson A. L. Rasmussen S. Stiverson K. Sharma M. Kirby
author_facet	M. Aminian T. Ghosh A. Peterson A. L. Rasmussen S. Stiverson K. Sharma M. Kirby
author_sort	M. Aminian
title	Early prognosis of respiratory virus shedding in humans
title_short	Early prognosis of respiratory virus shedding in humans
title_full	Early prognosis of respiratory virus shedding in humans
title_fullStr	Early prognosis of respiratory virus shedding in humans
title_full_unstemmed	Early prognosis of respiratory virus shedding in humans
title_sort	early prognosis of respiratory virus shedding in humans
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e5b962cbefe3457382315593b5185fa6
work_keys_str_mv	AT maminian earlyprognosisofrespiratoryvirussheddinginhumans AT tghosh earlyprognosisofrespiratoryvirussheddinginhumans AT apeterson earlyprognosisofrespiratoryvirussheddinginhumans AT alrasmussen earlyprognosisofrespiratoryvirussheddinginhumans AT sstiverson earlyprognosisofrespiratoryvirussheddinginhumans AT ksharma earlyprognosisofrespiratoryvirussheddinginhumans AT mkirby earlyprognosisofrespiratoryvirussheddinginhumans
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Early prognosis of respiratory virus shedding in humans

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