Descripción
- Edition: 1
- Latest edition
- Published: September 15, 2025
- No. of pages (Paperback): 464
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780443300103
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.
Key features
- 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
Readership
Table of contents
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
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