Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective.

algorithm classification/regression computational toxicity machine learning nanomaterials prediction

Journal

Environmental science & technology
ISSN: 1520-5851
Titre abrégé: Environ Sci Technol
Pays: United States
ID NLM: 0213155

Informations de publication

Date de publication:
07 Aug 2024
Historique:
medline: 7 8 2024
pubmed: 7 8 2024
entrez: 7 8 2024
Statut: aheadofprint

Résumé

The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore,

Identifiants

DOI: 10.1021/acs.est.4c03328 PMID: 39109992
pubmed: 39109992
doi: 10.1021/acs.est.4c03328
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Yunchi Zhou (Y)

School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
Ecole Centrale de Pékin/School of General Engineering, Beihang University, Beijing 100191, China.
ORCID: 0000-0001-6603-2177

Ying Wang (Y)

School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
ORCID: 0000-0002-1424-0422

Willie Peijnenburg (W)

Institute of Environmental Science, Leiden University, Leiden 2300 RA, The Netherlands.
National Institute of Public Health and the Environment, Center for Safety of Products and Substances, Bilthoven 3720BA, The Netherlands.

Martina G Vijver (MG)

Institute of Environmental Science, Leiden University, Leiden 2300 RA, The Netherlands.

Surendra Balraadjsing (S)

Institute of Environmental Science, Leiden University, Leiden 2300 RA, The Netherlands.
ORCID: 0000-0003-2167-8163

Zhaomin Dong (Z)

School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China.
ORCID: 0000-0002-0251-2355

Xiaoli Zhao (X)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
ORCID: 0000-0002-5671-389X

Kenneth M Y Leung (KMY)

State Key Laboratory of Marine Pollution and Department of Chemistry, City University of Hong Kong, Hong Kong 999077, China.

Holly M Mortensen (HM)

Public Health and Integrated Toxicology Division, Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States.

Zhenyu Wang (Z)

Institute of Environmental Process and Pollution Control, School of Environment & Ecology, Jiangnan University, Wuxi 214122, China.
ORCID: 0000-0002-5114-435X

Iseult Lynch (I)

School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom.
ORCID: 0000-0003-4250-4584

Antreas Afantitis (A)

Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus.
ORCID: 0000-0002-0977-8180

Yunsong Mu (Y)

School of Environment & Natural Resources, Renmin University of China, Beijing 100872, China.

Fengchang Wu (F)

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
ORCID: 0000-0001-8595-2243

Wenhong Fan (W)

School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China.
ORCID: 0000-0001-5047-1714

Classifications MeSH