Teaching

Before moving to UF, Dr. Lin taught two online courses: (1) Physiologically Based Pharmacokinetic Modeling (AP 873, every Spring semester), and (2) Basic and Applied Pharmacokinetics (AP 788, Fall of even years) at K-State.

At UF, Dr. Lin is teaching two courses: (1) Physiologically Based pharmacokinetic Modeling in Toxicology and Risk Assessment (PHC 7738C, online, every Fall semester), and (2) Artificial Intelligence in Environmental and Global Health (PHC 7636, in-person, every Spring semester). Please contact Dr. Lin for more information about these courses.

Textbook in the area of PBPK modeling in toxicology and risk assessment

Title: Physiologically Based Pharmacokinetic (PBPK) Modeling: Methods and Applications in Toxicology and Risk Assessment, 1st Edition

Editors: Jeffrey W. Fisher, Jeffrey M. Gearhart, and Zhoumeng Lin

Published Date: 1st August, 2020
Publisher: Elsevier
Page count: 348

Order at elsevier.com.

Supplementary Materials can be ordered at elsevier.com.

• Foreword: Melvin E. Andersen
• Preface: Jeffrey W. Fisher, Zhoumeng Lin, Jeffery M. Gearhart
• Chapter 1: Zhoumeng Lin, Jeffrey W. Fisher. A history and recent efforts of selected physiologically based pharmacokinetic modeling topics

• Chapter 2: Shruti V. Kabadi, Zhoumeng Lin. Introduction to classical pharmacokinetics

• Chapter 3: Jeffrey W. Fisher, Xiaoxia Yang, Darshan Mehta, Conrad Housand, Zhoumeng Lin. Fundamentals of physiologically based pharmacokinetic modeling

• Chapter 4: CE Hack, AY Efremenko, SN Pendse, CA Ellison, A Najjar, N Hewitt, A Schepky, HJ Clewell III. Physiologically based pharmacokinetic modeling software

• Chapter 5: Jeffrey W. Fisher, Jeffery M. Gearhart, Jerry L. Campbell, Jr, Darshan Mehta. Chemical absorption and writing code for portals of entry

• Chapter 6: Christopher Ruark. Physiologically based pharmacokinetic model: distribution processes

• Chapter 7: Jeffrey W. Fisher, Jerry L. Campbell, Jr, Zhoumeng Lin. Metabolism and physiologically based pharmacokinetic models

• Chapter 8: Sami Haddad, Andy Nong. Physiologically based pharmacokinetic model: excretion via urine, feces, and breath

• Chapter 9: Tammie R. Covington, Jeffery M. Gearhart. Sensitivity and Monte Carlo analysis techniques and their use in uncertainty, variability, and population analysis

• Chapter 10: Zhoumeng Lin, Yi-Hsien Cheng, Wei-Chun Chou, Miao Li. Physiologically based pharmacokinetic model calibration, evaluation, and performance assessment

• Chapter 11: Lisa M. Sweeney, Jeffery M. Gearhart. Examples of physiologically based pharmacokinetic modeling applied to risk assessment

• Chapter 12: Miyoung Yoon. Physiologically based pharmacokinetic models to support modernized chemical safety assessment

Textbook in the area of computational toxicology

Title: Machine Learning and Artificial Intelligence in Toxicology and Environmental Health, 1st Edition

Editors: Zhoumeng Lin, Wei-Chun Chou

Final Publication Year: 2026
Publisher: Elsevier
Page count: 438

Order at shop.elsevier.com.

Supplementary materials can be ordered at elsevier.com.

• Foreword: Nicole C. Kleinstreuer
• Preface: Zhoumeng Lin, Wei-Chun Chou
• Chapter 1: Zhoumeng Lin, Pei-Yu Wu, Wei-Chun Chou. Applications of machine learning and artificial intelligence in toxicology and environmental health
• Chapter 2: Wei-Chun Chou, Weitao Chen, Kunpeng Chen. Basics of machine learning and artificial intelligence methods in toxicology and environmental health

• Chapter 3: Wei-Chun Chou, Miao Li, Srijit Seal, Zhoumeng Lin. Application of machine learning and artificial intelligence methods in predictions of absorption, distribution, metabolism, and excretion properties of chemicals

• Chapter 4: Wei-Chun Chou, Miao Li, Zhoumeng Lin. Application of machine learning and artificial intelligence methods in physiologically based pharmacokinetic modeling

• Chapter 5: Fahad Mostafa, Minjun Chen. Machine learning and artificial intelligence methods for predicting liver toxicity

• Chapter 6: Hung-Lin Kan, Yu-Wen Chiu, Chun-Wei Tung. Metaclassifiers and multitask learning for predicting toxicity endpoints with complex mechanism

  

• Chapter 7: Qunshan Jia, George Daston. Application of machine learning and deep learning models in developmental toxicity

• Chapter 8: Zhoumeng Lin, Zhicheng Zhang, Chi-Yun Chen, Qiran Chen, Pei-Yu Wu. Application of machine learning and artificial intelligence methods in toxicity assessment of nanoparticles

• Chapter 9: Tong Wang, Xinyu Yang, Hao Zhu. ViNAS-Pro: Online nanotoxicity data, modeling, and predictions

• Chapter 10: Chih-Da Wu, Aji Kusumaning Asri. A geospatial artificial intelligence-based approach for precise air pollution estimation in support of health outcome analysis

• Chapter 11: Sumon Hossain Rabby, Xiuming Sun, Abdul Mobin Ibna Hafiz, Zhengxiao Yan, Syed Usama Imtiaz, Mitra Nasr Azadani, Maryam Pakdehi, Ali Salou Moumouni, Ebrahim Ahmadisharaf, Nasrin Alamdari

• Chapter 12: Kun Mi, Simone Marini, Zhoumeng Lin. Application of machine learning and artificial intelligence methods for predicting antimicrobial resistance
• Chapter 13: Zhoumeng Lin, Kun Mi, Xue Wu. Application of machine learning and artificial intelligence methods in food safety assessment

• Chapter 14: Chi-Yun Chen, Zhoumeng Lin. From data to decisions: Leveraging machine learning and artificial intelligence methods for human health risk assessment of environmental pollutants
• Chapter 15: Qiran Chen, Zhoumeng Lin. Application of machine learning and artificial intelligence methods in toxicity and risk assessment of chemical mixtures

• Chapter 16: Ted Smith. Generative artificial intelligence for research translation in environmental toxicology and the ethical considerations