Machine Learning and Artificial Intelligence in Toxicology and Environmental Health

Herausgeber: Lin, Zhoumeng; Chou, Wei-Chun

• Broschiertes Buch

Produktbeschreibung

Machine Learning and Artificial Intelligence in Toxicology and Environmental Health introduces the fundamental concepts and principles of machine learning and AI, providing clear explanations on applying these methods to toxicology and environmental health. The book delves into predictions of chemical ADMET properties, development of PBPK and QSAR models, toxicogenomic analysis, and the evaluation of high-throughput in vitro assays. It aims to guide readers in adapting machine learning and AI techniques to various research problems within these fields. Additionally, the text explores ecotoxicology assessment, impacts of air pollution, climate change, food safety, and chemical risk assessment. It includes case studies, hands-on computer exercises, and example codes, making it a comprehensive resource for researchers, academics, students, and industry professionals. The book highlights how AI can enhance risk assessment, predict environmental hazards, and speed up the identification of harmful substances.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 400
• Erscheinungstermin: 1. August 2025
• Englisch
• ISBN-13: 9780443300103
• ISBN-10: 0443300100
• Artikelnr.: 72649688

Inhaltsangabe

1. Applications of machine learning and artificial intelligence in
toxicology and environmental health
2. Basics of machine learning and artificial intelligence methods in
toxicology and environmental health
3. Application of machine learning and AI methods in predictions of
absorption, distribution, metabolism, excretion (ADME) properties
4. Application of machine learning and AI methods in developing
physiologically based pharmacokinetic (PBPK) models
5. Application of machine learning and AI methods in predictions of
different toxicity endpoints
6. Application of machine learning and AI methods in developing
quantitative structure-activity relationship (QSAR) models
7. Application of machine learning and AI methods in quantitative adverse
outcome pathway (qAOP) analysis
8. Application of machine learning and AI methods in toxicogenomics
analysis
9. Application of machine learning and AI methods in analyzing
high[1]throughput in vitro assays
10. Application of machine learning and AI methods in high-throughput cell
imaging and analysis
11. Application of machine learning and AI methods in exposure and toxicity
assessment of nanoparticles
12. Application of machine learning and AI methods in ecotoxicity
assessment
13. Application of machine learning and AI methods in air pollution
assessment and health outcome analysis
14. Application of machine learning and AI methods in climate changes and
health outcome analysis
15. Application of machine learning and AI methods in predicting health
outcomes based on human biomonitoring data
16. Databases for applications of machine learning and AI methods in
toxicology and environmental health
17. Application of machine learning and AI methods in food safety
assessment
18. Application of machine learning and AI methods in human health risk
assessment of environmental chemicals
19. Application of machine learning and AI methods in toxicity and risk
assessment of chemical mixtures
20. Data sharing, collaboration, challenges, and future direction of
machine learning and AI methods in toxicology and environmental health
21. Regulatory and Ethical Consideration of machine learning and AI methods
in toxicology and environmental health