Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coliproteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.

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Autores principales:	Minseung Kim, Navneet Rai, Violeta Zorraquino, Ilias Tagkopoulos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/99ed3ae0d5e148ff8d397f4572e0ceba
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spelling	oai:doaj.org-article:99ed3ae0d5e148ff8d397f4572e0ceba2021-12-02T17:32:20ZMulti-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli10.1038/ncomms130902041-1723https://doaj.org/article/99ed3ae0d5e148ff8d397f4572e0ceba2016-10-01T00:00:00Zhttps://doi.org/10.1038/ncomms13090https://doaj.org/toc/2041-1723Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coliproteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.Minseung KimNavneet RaiVioleta ZorraquinoIlias TagkopoulosNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-12 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Minseung Kim Navneet Rai Violeta Zorraquino Ilias Tagkopoulos Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
description	Multi-omics data integration is a great challenge. Here, the authors compile a database of E. coliproteomics, transcriptomics, metabolomics and fluxomics data to train models of recurrent neural network and constrained regression, enabling prediction of bacterial responses to perturbations.
format	article
author	Minseung Kim Navneet Rai Violeta Zorraquino Ilias Tagkopoulos
author_facet	Minseung Kim Navneet Rai Violeta Zorraquino Ilias Tagkopoulos
author_sort	Minseung Kim
title	Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
title_short	Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
title_full	Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
title_fullStr	Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
title_full_unstemmed	Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli
title_sort	multi-omics integration accurately predicts cellular state in unexplored conditions for escherichia coli
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/99ed3ae0d5e148ff8d397f4572e0ceba
work_keys_str_mv	AT minseungkim multiomicsintegrationaccuratelypredictscellularstateinunexploredconditionsforescherichiacoli AT navneetrai multiomicsintegrationaccuratelypredictscellularstateinunexploredconditionsforescherichiacoli AT violetazorraquino multiomicsintegrationaccuratelypredictscellularstateinunexploredconditionsforescherichiacoli AT iliastagkopoulos multiomicsintegrationaccuratelypredictscellularstateinunexploredconditionsforescherichiacoli
_version_	1718380322696986624

Multi-omics integration accurately predicts cellular state in unexplored conditions for Escherichia coli

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