Making big sense from big data in toxicology by read-across.
about
Evaluating the Impact of the U.S. National Toxicology Program: A Case Study on Hexavalent ChromiumCombination of multiple neural crest migration assays to identify environmental toxicants from a proof-of-concept chemical library.The Threshold of Toxicological Concern for prenatal developmental toxicity in rats and rabbits.Adverse outcome pathways: opportunities, limitations and open questionsGreen Toxicology: a strategy for sustainable chemical and material development.Computational approaches to chemical hazard assessment.Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibilityBig-data and machine learning to revamp computational toxicology and its use in risk assessment
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P2860
Making big sense from big data in toxicology by read-across.
description
2016 nî lūn-bûn
@nan
2016 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
Making big sense from big data in toxicology by read-across.
@ast
Making big sense from big data in toxicology by read-across.
@en
type
label
Making big sense from big data in toxicology by read-across.
@ast
Making big sense from big data in toxicology by read-across.
@en
prefLabel
Making big sense from big data in toxicology by read-across.
@ast
Making big sense from big data in toxicology by read-across.
@en
P356
P1476
Making big sense from big data in toxicology by read-across.
@en
P2093
Thomas Hartung
P356
10.14573/ALTEX.1603091
P577
2016-01-01T00:00:00Z