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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xv 7. Foreword - p. xix 8. Preface - p. xxi 9. Chapter 1. Applications of machine learning and artificial intelligence in toxicology and environmental health∗ - p. 1 10. Chapter 2. Basics of machine learning and artificial intelligence methods in toxicology and environmental health∗ - p. 27 11. Chapter 3. Application of machine learning and artificial intelligence methods in predictions of absorption, distribution, metabolism, and excretion properties of chemicals∗ - p. 61 12. Chapter 4. Application of machine learning and artificial intelligence methods in physiologically based pharmacokinetic modeling∗ - p. 99 13. Chapter 5. Machine learning and artificial intelligence methods for predicting liver toxicity∗ - p. 139 14. Chapter 6. Metaclassifiers and multitask learning for predicting toxicity endpoints with complex mechanism∗ - p. 153 15. Chapter 7. Application of machine learning and deep learning models in developmental toxicity∗ - p. 173 16. Chapter 8. Application of machine learning and artificial intelligence methods in toxicity assessment of nanoparticles∗ - p. 197 17. Chapter 9. ViNAS-Pro: Online nanotoxicity data, modeling, and predictions∗ - p. 233 18. Chapter 10. A geospatial artificial intelligence–based approach for precise air pollution estimation in support of health outcome analysis∗ - p. 245 19. Chapter 11. Application of machine learning methods in water quality modeling∗ - p. 271 20. Chapter 12. Application of machine learning and artificial intelligence methods for predicting antimicrobial resistance∗ - p. 311 21. Chapter 13. Application of machine learning and artificial intelligence methods in food safety assessment∗ - p. 333 22. Chapter 14. From data to decisions: Leveraging machine learning and artificial intelligence methods for human health risk assessment of environmental pollutants∗ - p. 353 23. Chapter 15. Application of machine learning and artificial intelligence methods in toxicity and risk assessment of chemical mixtures∗ - p. 395 24. Chapter 16. Generative artificial intelligence for research translation in environmental toxicology and the ethical considerations∗ - p. 421 25. Index - p. 433
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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xv 7. Foreword - p. xix 8. Preface - p. xxi 9. Chapter 1. Applications of machine learning and artificial intelligence in toxicology and environmental health∗ - p. 1 10. Chapter 2. Basics of machine learning and artificial intelligence methods in toxicology and environmental health∗ - p. 27 11. Chapter 3. Application of machine learning and artificial intelligence methods in predictions of absorption, distribution, metabolism, and excretion properties of chemicals∗ - p. 61 12. Chapter 4. Application of machine learning and artificial intelligence methods in physiologically based pharmacokinetic modeling∗ - p. 99 13. Chapter 5. Machine learning and artificial intelligence methods for predicting liver toxicity∗ - p. 139 14. Chapter 6. Metaclassifiers and multitask learning for predicting toxicity endpoints with complex mechanism∗ - p. 153 15. Chapter 7. Application of machine learning and deep learning models in developmental toxicity∗ - p. 173 16. Chapter 8. Application of machine learning and artificial intelligence methods in toxicity assessment of nanoparticles∗ - p. 197 17. Chapter 9. ViNAS-Pro: Online nanotoxicity data, modeling, and predictions∗ - p. 233 18. Chapter 10. A geospatial artificial intelligence–based approach for precise air pollution estimation in support of health outcome analysis∗ - p. 245 19. Chapter 11. Application of machine learning methods in water quality modeling∗ - p. 271 20. Chapter 12. Application of machine learning and artificial intelligence methods for predicting antimicrobial resistance∗ - p. 311 21. Chapter 13. Application of machine learning and artificial intelligence methods in food safety assessment∗ - p. 333 22. Chapter 14. From data to decisions: Leveraging machine learning and artificial intelligence methods for human health risk assessment of environmental pollutants∗ - p. 353 23. Chapter 15. Application of machine learning and artificial intelligence methods in toxicity and risk assessment of chemical mixtures∗ - p. 395 24. Chapter 16. Generative artificial intelligence for research translation in environmental toxicology and the ethical considerations∗ - p. 421 25. Index - p. 433
