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.
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
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