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Predictive Analytics For Toxicology Applications In Discovery Science | Valerio | 1st Edition, 2026 | Taylor & Francis Group (English Medium)
|
| 24053-24053 |
Predictive Analytics for Toxicology Applications in Discovery Science is a groundbreaking book that explores the intersection of predictive analytics and toxicology in the field of discovery science. Written by renowned author Valerio, this 1st edition book is set to be released in 2026 by Taylor & Francis Group. This comprehensive book delves into how predictive analytics can be applied to toxicology studies to predict the potential toxicity of chemical compounds and other substances. By leveraging advanced data analysis techniques, researchers and scientists can make more informed decisions about the safety of various compounds before they are brought to market. The book covers a wide range of topics related to predictive analytics in toxicology, including data collection and preprocessing, feature selection, model building, validation techniques, and interpretation of results. It also discusses the challenges and limitations of using predictive analytics in toxicology studies and provides practical guidance on how to overcome these obstacles. Readers will learn about the latest advancements in predictive analytics technology, including machine learning algorithms, deep learning models, and big data analytics. Case studies and real-world examples are included throughout the book to illustrate how predictive analytics can be successfully applied in toxicology research. Whether you are a seasoned researcher looking to expand your knowledge or a newcomer interested in learning more about predictive analytics in toxicology, this book is an invaluable resource that will help you stay at the forefront of discovery science.
Predictive Analytics For Toxicology Applications In Discovery Science (1st Edition, 2026) by Valerio is an advanced and highly specialized reference book that explores the integration of predictive analytics and toxicology in modern discovery science. Published by Taylor & Francis Group, this hardbound edition is an essential resource for researchers, scientists, pharmacologists, and professionals working in drug development, toxicology, and computational biology.
In today’s rapidly evolving scientific landscape, predictive analytics plays a crucial role in enhancing the efficiency and accuracy of toxicological assessments. This book provides a comprehensive overview of how data-driven approaches are transforming traditional toxicology into a more predictive and proactive discipline. It is particularly valuable for those involved in drug discovery, chemical safety evaluation, and risk assessment.
The book begins with foundational concepts in toxicology and predictive modeling, helping readers understand the core principles behind data analysis and its applications in biological systems. It then progresses into detailed discussions on machine learning, artificial intelligence (AI), and computational modeling techniques used to predict toxicological outcomes. These approaches reduce the need for extensive animal testing and accelerate the drug development process.
One of the key highlights of this book is its focus on in silico toxicology, where computer-based simulations are used to predict the safety and efficacy of chemical compounds. The author explains how predictive models can identify potential toxic effects early in the discovery phase, saving time, cost, and resources in pharmaceutical research.
The text also covers important topics such as quantitative structure-activity relationship (QSAR) models, big data analytics, and bioinformatics tools. These technologies enable scientists to analyze large datasets and uncover patterns that are critical for understanding toxicity mechanisms. Real-world case studies and practical examples are included to demonstrate how these methods are applied in research and industry settings.
In addition to technical insights, the book addresses regulatory perspectives and ethical considerations in predictive toxicology. It highlights global guidelines and standards that govern the use of computational models in safety assessment, making it highly relevant for professionals working in regulatory affairs and compliance.
Another significant aspect of this book is its interdisciplinary approach, combining elements of pharmacology, data science, chemistry, and biology. This makes it suitable for a wide audience, including postgraduate students, academicians, and industry experts seeking to expand their knowledge in cutting-edge toxicological research.
With its detailed content and approximately well-structured chapters, this hardbound edition ensures durability and long-term reference value. The language is technical yet clear, allowing readers to grasp complex concepts effectively.
Overall, Predictive Analytics For Toxicology Applications In Discovery Science is a must-have resource for anyone looking to understand the future of toxicology and its role in safer and more efficient drug discovery. It provides valuable insights into how predictive analytics is revolutionizing the field and shaping the next generation of scientific innovation.
| SKU / BOOK Code: | PT-9780367775544-2026-HB |
| Publisher: | Taylor & Francis Group |
| Author: | Valerio |
| Binding Type: | Hardcover |
| No. of Pages: | 272 |
| ISBN-10: | NA |
| ISBN-13: | NA |
| Edition: | 1st |
| Language: | English Medium |
| Publish Year: | 2026-01 |
| Weight (g): | 300 |
| Product Condition: | New |
| Reading Age: | Above 18 Years |
| Country of Origin: | India |
| Genre: | Medical Books |
| Manufacturer: | Taylor & Francis Group |
| Importer: | Taylor & Francis Group |
| Packer: | Fullfilled by Supplier |
Predictive Analytics for Toxicology Applications in Discovery Science is a groundbreaking book that explores the intersection of predictive analytics and toxicology in the field of discovery science. Written by renowned author Valerio, this 1st edition book is set to be released in 2026 by Taylor & Francis Group. This comprehensive book delves into how predictive analytics can be applied to toxicology studies to predict the potential toxicity of chemical compounds and other substances. By leveraging advanced data analysis techniques, researchers and scientists can make more informed decisions about the safety of various compounds before they are brought to market. The book covers a wide range of topics related to predictive analytics in toxicology, including data collection and preprocessing, feature selection, model building, validation techniques, and interpretation of results. It also discusses the challenges and limitations of using predictive analytics in toxicology studies and provides practical guidance on how to overcome these obstacles. Readers will learn about the latest advancements in predictive analytics technology, including machine learning algorithms, deep learning models, and big data analytics. Case studies and real-world examples are included throughout the book to illustrate how predictive analytics can be successfully applied in toxicology research. Whether you are a seasoned researcher looking to expand your knowledge or a newcomer interested in learning more about predictive analytics in toxicology, this book is an invaluable resource that will help you stay at the forefront of discovery science.
Predictive Analytics For Toxicology Applications In Discovery Science (1st Edition, 2026) by Valerio is an advanced and highly specialized reference book that explores the integration of predictive analytics and toxicology in modern discovery science. Published by Taylor & Francis Group, this hardbound edition is an essential resource for researchers, scientists, pharmacologists, and professionals working in drug development, toxicology, and computational biology.
In today’s rapidly evolving scientific landscape, predictive analytics plays a crucial role in enhancing the efficiency and accuracy of toxicological assessments. This book provides a comprehensive overview of how data-driven approaches are transforming traditional toxicology into a more predictive and proactive discipline. It is particularly valuable for those involved in drug discovery, chemical safety evaluation, and risk assessment.
The book begins with foundational concepts in toxicology and predictive modeling, helping readers understand the core principles behind data analysis and its applications in biological systems. It then progresses into detailed discussions on machine learning, artificial intelligence (AI), and computational modeling techniques used to predict toxicological outcomes. These approaches reduce the need for extensive animal testing and accelerate the drug development process.
One of the key highlights of this book is its focus on in silico toxicology, where computer-based simulations are used to predict the safety and efficacy of chemical compounds. The author explains how predictive models can identify potential toxic effects early in the discovery phase, saving time, cost, and resources in pharmaceutical research.
The text also covers important topics such as quantitative structure-activity relationship (QSAR) models, big data analytics, and bioinformatics tools. These technologies enable scientists to analyze large datasets and uncover patterns that are critical for understanding toxicity mechanisms. Real-world case studies and practical examples are included to demonstrate how these methods are applied in research and industry settings.
In addition to technical insights, the book addresses regulatory perspectives and ethical considerations in predictive toxicology. It highlights global guidelines and standards that govern the use of computational models in safety assessment, making it highly relevant for professionals working in regulatory affairs and compliance.
Another significant aspect of this book is its interdisciplinary approach, combining elements of pharmacology, data science, chemistry, and biology. This makes it suitable for a wide audience, including postgraduate students, academicians, and industry experts seeking to expand their knowledge in cutting-edge toxicological research.
With its detailed content and approximately well-structured chapters, this hardbound edition ensures durability and long-term reference value. The language is technical yet clear, allowing readers to grasp complex concepts effectively.
Overall, Predictive Analytics For Toxicology Applications In Discovery Science is a must-have resource for anyone looking to understand the future of toxicology and its role in safer and more efficient drug discovery. It provides valuable insights into how predictive analytics is revolutionizing the field and shaping the next generation of scientific innovation.
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