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.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 400
• Erscheinungstermin: 1. August 2025
• Englisch
• Gewicht: 450g
• 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 artificial intelligence methods in
predictions of absorption, distribution, metabolism, excretion properties
of chemicals
4. Application of machine learning and artificial intelligence methods in
physiologically based pharmacokinetic modeling
5. Machine learning and artificial intelligence methods for predicting
liver toxicity
6. Metaclassifiers and multitask learning for predicting toxicity endpoints
with complex mechanism
7. Application of machine learning and artificial intelligence methods in
developmental toxicity
8. Application of machine learning and artificial intelligence methods in
toxicity assessment of nanoparticles
9. ViNAS-Pro: online nanotoxicity data, modeling, and predictions
10. A geospatial artificial intelligence-based approach for precision air
pollution estimation in support of health outcome analysis
11. Application of machine learning methods in water quality modeling
12. Application of machine learning and artificial intelligence methods for
predicting antimicrobial resistance
13. Application of machine learning and artificial intelligence methods in
food safety assessment
14. From data to decisions: Leveraging machine learning and artificial
intelligence methods for human health risk assessment of environmental
pollutants
15. Application of machine learning and artificial intelligence methods in
toxicity and risk assessment of chemical mixtures
16. Generative artificial intelligence for research translation in
environmental toxicology and the ethical considerations