Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization

Large-scale in vitro assays may reduce the number of toxicological tests carried out in animals. Here, Huang et al. report a large dataset containing results of in vitrotests of approximately 10,000 chemicals, and use these data to create models that can potentially predict toxicity in humans.

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Autores principales:	Ruili Huang, Menghang Xia, Srilatha Sakamuru, Jinghua Zhao, Sampada A. Shahane, Matias Attene-Ramos, Tongan Zhao, Christopher P. Austin, Anton Simeonov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/6aedca7233ad4d39b830fe9c531524db
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spelling	oai:doaj.org-article:6aedca7233ad4d39b830fe9c531524db2021-12-02T17:31:20ZModelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization10.1038/ncomms104252041-1723https://doaj.org/article/6aedca7233ad4d39b830fe9c531524db2016-01-01T00:00:00Zhttps://doi.org/10.1038/ncomms10425https://doaj.org/toc/2041-1723Large-scale in vitro assays may reduce the number of toxicological tests carried out in animals. Here, Huang et al. report a large dataset containing results of in vitrotests of approximately 10,000 chemicals, and use these data to create models that can potentially predict toxicity in humans.Ruili HuangMenghang XiaSrilatha SakamuruJinghua ZhaoSampada A. ShahaneMatias Attene-RamosTongan ZhaoChristopher P. AustinAnton SimeonovNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-10 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ruili Huang Menghang Xia Srilatha Sakamuru Jinghua Zhao Sampada A. Shahane Matias Attene-Ramos Tongan Zhao Christopher P. Austin Anton Simeonov Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization
description	Large-scale in vitro assays may reduce the number of toxicological tests carried out in animals. Here, Huang et al. report a large dataset containing results of in vitrotests of approximately 10,000 chemicals, and use these data to create models that can potentially predict toxicity in humans.
format	article
author	Ruili Huang Menghang Xia Srilatha Sakamuru Jinghua Zhao Sampada A. Shahane Matias Attene-Ramos Tongan Zhao Christopher P. Austin Anton Simeonov
author_facet	Ruili Huang Menghang Xia Srilatha Sakamuru Jinghua Zhao Sampada A. Shahane Matias Attene-Ramos Tongan Zhao Christopher P. Austin Anton Simeonov
author_sort	Ruili Huang
title	Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization
title_short	Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization
title_full	Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization
title_fullStr	Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization
title_full_unstemmed	Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization
title_sort	modelling the tox21 10 k chemical profiles for in vivo toxicity prediction and mechanism characterization
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/6aedca7233ad4d39b830fe9c531524db
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Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization

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