Overview
- Editors:
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Huixiao Hong
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Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
- Covers comprehensive view of the machine learning and deep learning algorithms, methods, and software tools
- Provides many practical applications of machine learning and deep learning techniques in predictive toxicology
- Presents numerous figures to detail the diverse procedures used for variety of machine learning and deep learning
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Table of contents (28 chapters)
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- Rebecca Kusko, Huixiao Hong
Pages 1-17
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Machine Learning and Deep Learning Methods for Computational Toxicology
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- Dmitry Filimonov, Alexander Dmitriev, Anastassia Rudik, Vladimir Poroikov
Pages 21-51
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- Yi Zhong, Shanshan Wang, Gaozheng Li, Ji Yang, Zuquan Weng, Heng Luo
Pages 53-82
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- Yunyi Wu, Shenghui Guan, Guanyu Wang
Pages 83-140
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- Wei Shi, Rong Zhang, Haoyue Tan
Pages 141-157
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- Youjun Xu, Chia-Han Chou, Ningsheng Han, Jianfeng Pei, Luhua Lai
Pages 159-182
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- Thilini V. Mahanama, Arpan Biswas, Dong Wang
Pages 183-198
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- Cecile Valsecchi, Francesca Grisoni, Viviana Consonni, Davide Ballabio, Roberto Todeschini
Pages 199-220
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Tools and Approaches Facilitating Machine Learning and Deep Learning Methods in Computational Toxicology
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Front Matter
Pages 221-221
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- Dimitra-Danai Varsou, Andreas Tsoumanis, Anastasios G. Papadiamantis, Georgia Melagraki, Antreas Afantitis
Pages 223-242
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- Xianhai Yang, Huihui Liu, Rebecca Kusko, Huixiao Hong
Pages 243-262
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- Sundar Thangapandian, Gabriel Idakwo, Joseph Luttrell, Huixiao Hong, Chaoyang Zhang, Ping Gong
Pages 263-295
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- Huixiao Hong, Jie Liu, Weigong Ge, Sugunadevi Sakkiah, Wenjing Guo, Gokhan Yavas et al.
Pages 297-321
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- Zhongyu Wang, Jingwen Chen
Pages 323-353
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- Yuan Liu, Jinzhu Lin, Menglong Li, Zhining Wen
Pages 375-403
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Machine Learning and Deep Learning for Chemical Toxicity Prediction
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Front Matter
Pages 405-405
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- Kevin P. Cross, Glenn J. Myatt
Pages 407-432
About this book
This book is a collection of machine learning and deep learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art machine learning and deep learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those machine learning and deep learning algorithms, methods, and software tools and summarise the applications of machine learning and deep learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of machine learning and deep learning in toxicological research that serve as examples for readers to learn how to apply machine learning and deep learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of machine learning anddeep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students. The main benefit for the readers is understanding the widely used machine learning and deep learning techniques and gaining practical procedures for applying machine learning and deep learning in predictive toxicology.
Editors and Affiliations
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Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, USA
Huixiao Hong
About the editor
Huixiao Hong is a Senior Biomedical Research and Biomedical Product Assessment Service (SBRBPAS) expert and the chief of Bioinformatics Branch, Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration (FDA), working on the scientific bases for regulatory applications of bioinformatics, cheminformatics, artificial intelligence, and genomics. Before joining the FDA, he was the manager of Bioinformatics Division of Z-Tech, an ICFI company. He held a research scientist position at Sumitomo Chemical Company in Japan and was a visiting scientist at National Cancer Institute at National Institutes of Health. He was also an associate professor and the director of Laboratory of Computational Chemistry at Nanjing University in China. Dr. Hong is a member of steering committee of OpenTox, a member of the board directors of US MidSouth Computational Biology and Bioinformatics Society, and in the leadership circle of US FDA modeling and simulation working group. He published more than 240 scientific papers with a Google Scholar h-index 60. He serves as an associate editor for Experimental Biology and Medicine and an editorial board member for multiple peer-reviewed journals. He received his Ph.D. from Nanjing University in China and conducted research in Leeds University in England.
