Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Personalized prediction of tumor radiosensitivity would facilitate development of precision medicine workflows for cancer treatment. Here, the authors integrate machine learning and genome-scale metabolic modeling approaches to identify multi-omics biomarkers predictive of radiation response.

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Detalles Bibliográficos
Autores principales:	Joshua E. Lewis, Melissa L. Kemp
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/9cbb236c9a47446fa4b5be71053e21f2
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spelling	oai:doaj.org-article:9cbb236c9a47446fa4b5be71053e21f22021-12-02T17:01:56ZIntegration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance10.1038/s41467-021-22989-12041-1723https://doaj.org/article/9cbb236c9a47446fa4b5be71053e21f22021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22989-1https://doaj.org/toc/2041-1723Personalized prediction of tumor radiosensitivity would facilitate development of precision medicine workflows for cancer treatment. Here, the authors integrate machine learning and genome-scale metabolic modeling approaches to identify multi-omics biomarkers predictive of radiation response.Joshua E. LewisMelissa L. KempNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Joshua E. Lewis Melissa L. Kemp Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
description	Personalized prediction of tumor radiosensitivity would facilitate development of precision medicine workflows for cancer treatment. Here, the authors integrate machine learning and genome-scale metabolic modeling approaches to identify multi-omics biomarkers predictive of radiation response.
format	article
author	Joshua E. Lewis Melissa L. Kemp
author_facet	Joshua E. Lewis Melissa L. Kemp
author_sort	Joshua E. Lewis
title	Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
title_short	Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
title_full	Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
title_fullStr	Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
title_full_unstemmed	Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
title_sort	integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9cbb236c9a47446fa4b5be71053e21f2
work_keys_str_mv	AT joshuaelewis integrationofmachinelearningandgenomescalemetabolicmodelingidentifiesmultiomicsbiomarkersforradiationresistance AT melissalkemp integrationofmachinelearningandgenomescalemetabolicmodelingidentifiesmultiomicsbiomarkersforradiationresistance
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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

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