Predictive Analytics for Toxicology:Applications in Discovery Science

by: Luis G. Valerio Jr. (Author)

Publisher: CRC Press

Edition: 1st

Publication Date: 2024/8/13

Language: English

Print Length: 238 pages

ISBN-10: 0367775549

ISBN-13: 9780367775544

Book Description

Predictive data science is already in use in many fields, but its application in toxicology is new and sought after by non-animal alteative testing initiatives. Predictive Analytics for Toxicology:Applications in Discovery Science provides a comprehensive overview of the application of predictive analytics in the field of toxicology, highlighting its role and applications in discovery science.This book addresses the challenges of accurately predicting high-level endpoints of toxicity and explores the use of computational and artificial intelligence research to automate predictive toxicology. It underscores the importance of predictive toxicology in proposing and explaining adverse outcomes resulting from human exposures to specific toxicants, especially when experimental and observational data on the toxicant are incomplete or unavailable.Key features:Includes a plain language description of predictive analytics in toxicology adding an overview of the wide range of applicationsExamines the science of prediction, computational models as an automated science and comprehensive discussions on concepts of machine leaingOpens the hood on AI and its applications in toxicologyFeatures coverage on how in silico toxicity predictions are translational science toolsThe book integrates strategies and practices of predictive toxicology and offers practical information that students and professionals of the toxicology, chemical, and pharmaceutical industries will find essential. It fulfills the expectations of student researchers seeking to lea predictive analytics in toxicology. This book will energize scientists to conduct predictive toxicology modeling using artificial intelligence and machine leaing, and inspire students and seasoned scientists interested in automated science to pick up new research using predictive in silico models to evaluate chemical-induced toxicity.With its focus on practical applications and real-world examples, this book serves as a guide for navigating the complex issues and practices of discovery toxicology. It is an essential resource for those interested in computer-based methods in toxicology, providing valuable insights into the use of predictive analytics.

About the Author

Predictive data science is already in use in many fields, but its application in toxicology is new and sought after by non-animal alteative testing initiatives. Predictive Analytics for Toxicology:Applications in Discovery Science provides a comprehensive overview of the application of predictive analytics in the field of toxicology, highlighting its role and applications in discovery science.This book addresses the challenges of accurately predicting high-level endpoints of toxicity and explores the use of computational and artificial intelligence research to automate predictive toxicology. It underscores the importance of predictive toxicology in proposing and explaining adverse outcomes resulting from human exposures to specific toxicants, especially when experimental and observational data on the toxicant are incomplete or unavailable.Key features:Includes a plain language description of predictive analytics in toxicology adding an overview of the wide range of applicationsExamines the science of prediction, computational models as an automated science and comprehensive discussions on concepts of machine leaingOpens the hood on AI and its applications in toxicologyFeatures coverage on how in silico toxicity predictions are translational science toolsThe book integrates strategies and practices of predictive toxicology and offers practical information that students and professionals of the toxicology, chemical, and pharmaceutical industries will find essential. It fulfills the expectations of student researchers seeking to lea predictive analytics in toxicology. This book will energize scientists to conduct predictive toxicology modeling using artificial intelligence and machine leaing, and inspire students and seasoned scientists interested in automated science to pick up new research using predictive in silico models to evaluate chemical-induced toxicity.With its focus on practical applications and real-world examples, this book serves as a guide for navigating the complex issues and practices of discovery toxicology. It is an essential resource for those interested in computer-based methods in toxicology, providing valuable insights into the use of predictive analytics.