Machine learning for mixture toxicity analysis based on high-throughput printing technology.

Duan, Qiannan; Hu, Yuan; Zheng, Shourong; Lee, Jianchao; Chen, Jiayuan; Bi, Sifan; Xu, Zhaoyi

Duan, Qiannan; Hu, Yuan; Zheng, Shourong; Lee, Jianchao; Chen, Jiayuan; Bi, Sifan; Xu, Zhaoyi.

Afiliação

Duan Q; State Key Laboratory of Pollution Control and Resource Reuse, Jiangsu Key Laboratory of Vehicle Emissions Control, School of the Environment, Nanjing University, Nanjing 210023, China.
Hu Y; State Key Laboratory of Pollution Control and Resource Reuse, Jiangsu Key Laboratory of Vehicle Emissions Control, School of the Environment, Nanjing University, Nanjing 210023, China.
Zheng S; State Key Laboratory of Pollution Control and Resource Reuse, Jiangsu Key Laboratory of Vehicle Emissions Control, School of the Environment, Nanjing University, Nanjing 210023, China.
Lee J; Department of Environment Science, Shaanxi Normal University, Xi'an 710062, China.
Chen J; Department of Environment Science, Shaanxi Normal University, Xi'an 710062, China.
Bi S; Department of Environment Science, Shaanxi Normal University, Xi'an 710062, China.
Xu Z; State Key Laboratory of Pollution Control and Resource Reuse, Jiangsu Key Laboratory of Vehicle Emissions Control, School of the Environment, Nanjing University, Nanjing 210023, China. Electronic address: zhaoyixu@nju.edu.cn.

Talanta ; 207: 120299, 2020 Jan 15.

Article em En | MEDLINE | ID: mdl-31594611

Assuntos

Aprendizado de Máquina; Impressão; Testes de Toxicidade/instrumentação

Palavras-chave

High-throughput experiment; Inkjet printing; Machine learning; Mixture toxicity

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Impressão / Testes de Toxicidade / Aprendizado de Máquina Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

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