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Predictive and explanatory themes of NOAEL through a systematic comparison of different machine learning methods and descriptors.
Qian, Jie; Song, Fang-Liang; Liang, Rui; Wang, Xue-Jie; Liang, Ying; Dong, Jie; Zeng, Wen-Bin.
Afiliação
  • Qian J; Molecular Nutrition Branch, National Engineering Research Center of Rice and By-Product Deep Processing, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. Electronic address: evleij@163.com.
  • Song FL; Molecular Nutrition Branch, National Engineering Research Center of Rice and By-Product Deep Processing, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. Electronic address: liangsnfa@163.com.
  • Liang R; Molecular Nutrition Branch, National Engineering Research Center of Rice and By-Product Deep Processing, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. Electronic address: lr1303432292@163.com.
  • Wang XJ; Molecular Nutrition Branch, National Engineering Research Center of Rice and By-Product Deep Processing, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. Electronic address: 249795581@qq.com.
  • Liang Y; Molecular Nutrition Branch, National Engineering Research Center of Rice and By-Product Deep Processing, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, 410004, PR China. Electronic address: liangying498@163.com.
  • Dong J; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, PR China. Electronic address: jiedong@csu.edu.cn.
  • Zeng WB; Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, PR China. Electronic address: wbzeng@hotmail.com.
Food Chem Toxicol ; 168: 113325, 2022 Oct.
Article em En | MEDLINE | ID: mdl-35963474
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cosméticos Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Revista: Food Chem Toxicol Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Cosméticos Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Revista: Food Chem Toxicol Ano de publicação: 2022 Tipo de documento: Article