Machine Learning and Artificial Intelligence in Toxicology and Environmental Health
- 1st Edition - September 15, 2025
- Latest edition
- Editors: Zhoumeng Lin, Wei-Chun Chou
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 1 0 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 1 1 - 0
Machine Learning and Artificial Intelligence in Toxicology and Environmental Health introduces the fundamental concepts and principles of machine learning and AI, providing clear… Read more
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.
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.
- 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 help readers apply machine learning and artificial intelligence (AI) methods in toxicology and environmental health
Researchers, faculty, postdocs, advanced undergraduate, graduate students in the fields of toxicology, environmental health, pharmacology, biostatistics, bioinformatics, epidemiology, bioengineering; Industry professionals from consulting companies, pharmaceutical and chemical industry, and state and federal public health agencies
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
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
- Edition: 1
- Latest edition
- Published: September 15, 2025
- Language: English
ZL
Zhoumeng Lin
Dr. Zhoumeng Lin is an Assistant Professor of Pharmacology and Toxicology in the Institute of Computational Comparative Medicine (ICCM), Department of Anatomy and Physiology, College of Veterinary Medicine, Kansas State University. He is a Diplomate of American Board of Toxicology (DABT), the Coordinator of the Certara Center of Excellence for Model-informed Drug Development at Kansas State University, and the Principal Investigator at the Midwest Regional Center of the Food Animal Residue Avoidance Databank (FARAD) program. Dr. Lin has more than 8 years of research experience in PBPK modeling for environmental chemicals, drugs, and nanoparticles in laboratory rodents, food-producing animals, companion animals and humans. He received graduate training in Toxicology and PBPK modeling from Dr. Nikolay M. Filipov and Dr. Jeffrey W. Fisher at The University of Georgia. He received postdoc training in Pharmacology, Toxicology, and PBPK modeling from Dr. Jim E. Riviere, Dr. Nancy A. Monteiro-Riviere and Dr. Ronette Gehring at Kansas State University. He learned how to teach PBPK modeling from the PBPK Modeling Workshop for Beginners offered by Dr. Raymond S. H. Yang at Colorado State University. His current research focuses on developing PBPK models and other computational methods to address issues in food safety assessment, toxicology, and risk assessment. He teaches an online course entitled “Physiologically Based Pharmacokinetic Modeling” every Spring semester and another online course entitled “Basic and Applied Pharmacokinetics” in the Fall semester through K-State Global Campus.
Affiliations and expertise
Associate Professor, Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, USAWC
Wei-Chun Chou
Dr. Wei-Chun Chou is a Research Assistant Professor of the Department of Environmental and Global Health and a member of the Center for Environmental and Human Toxicology (CEHT) at the University of Florida. He received his PhD in Biomedical Engineering and Environmental Sciences from the National Tsing Hua University, Taiwan in 2013. He completed his postdoctoral training in the Institute of Computational Comparative Medicine at Kansas State University in 2021. His research focused on the development of computational models for prediction of chemical toxicity and its application on human health risk assessments without resorting to animal testing. The goals are accomplished by integrating in vitro high-throughput toxicity screening data, physiologically based pharmacokinetic (PBPK) modeling, machine learning and artificial intelligence to quantitatively describe the relationships between environmental exposure and mechanisms that cause adverse effects in human populations. He has received several awards and honors from the Society of Toxicology (SOT), including the Andersen-Clewell Trainee Award of the Biological Modeling Specialty Section and Best Paper Award of Risk Assessment Specialty Section.
Affiliations and expertise
Research Assistant Professor, Center for Environmental and Human Toxicology, College, University of Florida, Gainesville, USARead Machine Learning and Artificial Intelligence in Toxicology and Environmental Health on ScienceDirect