Developing random forest based QSAR models for predicting the mixture toxicity of TiO<sub>2</sub> based nano-mixtures to Daphnia magna.

Trinh, Tung X; Seo, Myungwon; Yoon, Tae Hyun; Kim, Jongwoon

Developing random forest based QSAR models for predicting the mixture toxicity of TiO₂ based nano-mixtures to Daphnia magna.

Trinh, Tung X; Seo, Myungwon; Yoon, Tae Hyun; Kim, Jongwoon.

Afiliação

Trinh TX; Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Republic of Korea; Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea.
Seo M; Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Republic of Korea.
Yoon TH; Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Republic of Korea; Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Republic of Korea.
Kim J; Chemical Safety Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Republic of Korea. Electronic address: jkim@krict.re.kr.

NanoImpact ; 25: 100383, 2022 01.

Article em En | MEDLINE | ID: mdl-35559889

Assuntos

Daphnia; Poluentes Químicos da Água; Animais; Cádmio/farmacologia; Compostos Orgânicos/farmacologia; Relação Quantitativa Estrutura-Atividade; Titânio; Poluentes Químicos da Água/toxicidade

Palavras-chave

Daphnia magna; Machine learning; QSAR; TiO(2) nano-mixture; Toxicity

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Poluentes Químicos da Água / Daphnia Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: NanoImpact Ano de publicação: 2022 Tipo de documento: Article

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