Predictive Analytics for Toxicology: Applications in Discovery Science (Original Publisher)

Predictive Analytics for Toxicology: Applications in Discovery Science (1st Edition, 2024) is a groundbreaking reference for researchers, toxicologists, pharmacologists, and data scientists seeking to harness the full potential of predictive analytics in biomedical discovery. Published by CRC Press, this authoritative volume explores the integration of data science, machine learning, and toxicology to revolutionize early-stage drug development and risk assessment.

Designed for professionals and advanced students in the fields of pharmacology, bioinformatics, computational biology, and toxicology, this book serves as both a comprehensive guide and a practical toolkit for applying analytics in toxicological research.

Key Features and Highlights:

• A cutting-edge resource bridging toxicology with predictive analytics and AI.
• Explains methodologies for data mining, machine learning, and statistical modeling in toxicological contexts.
• Covers case studies that illustrate real-world applications in drug safety and chemical risk prediction.
• Addresses regulatory frameworks and best practices for implementing predictive models in pharmaceutical R&D.
• Authored by experts in computational toxicology with a focus on discovery science applications.

Major Topics Covered:

• Includes core chapters on predictive modeling techniques, machine learning in toxicology, big data integration, biomarker discovery, and AI-driven safety evaluations.
• Explores applications in early drug discovery, environmental health, and translational toxicology.
• Discusses tools and platforms for data analysis, validation strategies, and ethical considerations.

About the Author:
Written by leading researchers in the field of computational toxicology, this volume reflects years of experience in applying predictive models to biomedical data and regulatory science.

Technical Details:

• File Format:  PDF
• File Size: Approx. 12 MB
• Language: English
• Publisher: CRC Press, 2024
• Device Compatibility: Compatible with all major e-readers, tablets, and desktop PDF
• Frequently Asked Questions (FAQs)

Q1: Is this book suitable for readers without a background in machine learning?
A1: While prior knowledge of basic analytics is helpful, the book provides foundational explanations and real-world case studies, making it accessible for professionals transitioning into predictive toxicology from related scientific fields.

Q2: Can I use the insights from this book for regulatory submissions or pharmaceutical applications?
A2: Yes. The book addresses regulatory frameworks and includes practical guidance on how predictive models can be used to support decision-making in drug safety and early discovery pipelines.