A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods-Reference-Cited by-同舟云学术

A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods

Published:2022-02 Issue: Volume:160 Page:112802
ISSN:0278-6915
Container-title:Food and Chemical Toxicology
language:en
Short-container-title:Food and Chemical Toxicology

Author:

Wang Chia-Chi,Liang Yu-Chih,Wang Shan-Shan,Lin Pinpin,Tung Chun-Wei^ORCID

Funder

National Health Research Institutes

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Subject

Toxicology,General Medicine,Food Science

Reference38 articles.

1. VEGA-QSAR: AI inside a platform for predictive toxicology;Benfenati;CEUR Workshop Proc,2013

2. Data-based review of QSARs for predicting genotoxicity: the state of the art;Benigni;Mutagenesis,2019

3. Pred-skin: a web portal for accurate prediction of human skin sensitizers;Borba;Chem. Res. Toxicol.,2021

4. Random forests;Breiman;Mach. Learn.,2001

5. Hazard prioritisation of substances in printing inks and adhesives applied to plastic food packaging;Galbiati;Food Addit. Contam. Part Chem. Anal. Control Expo. Risk Assess.,2021

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