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Machine Learning and Artificial Intelligence in Toxicology and Environmental Health de Zhoumeng Lin

Machine Learning and Artificial Intelligence in Toxicology and Environmental Health

Editat de Zhoumeng Lin, Wei-Chun Chou
en Limba Engleză Paperback – aug 2025
Machine Learning and Artificial Intelligence in Toxicology and Environmental Health introduces the fundamental concepts and principles of machine learning and AI as well as provide explanations on how to apply machine learning and AI methods to the study toxicology and environmental health. The book explores topics such as predictions of chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, the development of physiologically based pharmacokinetic (PBPK) and quantitative structure-activity relationship (QSAR) models, toxicogenomic analysis, analysis of high-throughput in vitro assays, ecotoxicology assessment, air pollution, climate change, food safety and chemical risk assessment. It also punctuates these topics with case studies, hands-on computer exercises, examples codes. Machine Learning and Artificial Intelligence in Toxicology and Environmental Health is a comprehensive and accessible source that provides researchers, academics, advanced undergraduate and graduate students, and industry professionals with guidance on how to adapt machine learning and AI methods to address specific research problems in toxicology and environmental health and also enables them to learn more about how AI can enhance risk assessment, predict environmental hazards, and expedite the identification of potentially harmful substances.

  • Covers the basic concepts and principles of commonly used machine learning and AI methods in the field of toxicology and environmental health
  • Provides an introduction to the applications of machine learning and AI methods in toxicology and environmental health
  • Offers case studies, example codes, and hands-on computer exercises to learn how to apply machine learning and artificial intelligence (AI) methods in toxicology and environmental health
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Specificații

ISBN-13: 9780443300103
ISBN-10: 0443300100
Pagini: 400
Dimensiuni: 191 x 235 mm
Editura: ELSEVIER SCIENCE

Cuprins

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