The rate at which toxicological data is generated is continually becoming more rapid and the volume of data generated is growing dramatically. This is due in part to advances in software solutions and cheminformatics approaches which increase the availability of open data from chemical, biological and toxicological and high throughput screening resources. However, the amplified pace and capacity of data generation achieved by these novel techniques presents challenges for organising and analysing data output. Big Data in Predictive Toxicology discusses these challenges as well as the opportunities of new techniques encountered in data science. It addresses the nature of toxicological big data, their storage, analysis and interpretation. It also details how these data can be applied in toxicity prediction, modelling and risk assessment. This title is of particular relevance to researchers and postgraduates working and studying in the fields of computational methods, applied and physical chemistry, cheminformatics, biological sciences, predictive toxicology and safety and hazard assessment.

Predictive data science is already in use in many fields, but its application in toxicology is new and sought after by non-animal alternative 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 applications Examines the science of prediction, computational models as an automated science and comprehensive discussions on concepts of machine learning Opens the hood on AI and its applications in toxicology Features coverage on how in silico toxicity predictions are translational science tools The 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 learn predictive analytics in toxicology. This book will energize scientists to conduct predictive toxicology modeling using artificial intelligence and machine learning, 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.

Big Data Analytics in Chemoinformatics and Bioinformatics: With Applications to Computer-Aided Drug Design, Cancer Biology, Emerging Pathogens and Computational Toxicology provides an up-to-date presentation of big data analytics methods and their applications in diverse fields. The proper management of big data for decision-making in scientific and social issues is of paramount importance. This book gives researchers the tools they need to solve big data problems in these fields. It begins with a section on general topics that all readers will find useful and continues with specific sections covering a range of interdisciplinary applications. Here, an international team of leading experts review their respective fields and present their latest research findings, with case studies used throughout to analyze and present key information. - Brings together the current knowledge on the most important aspects of big data, including analysis using deep learning and fuzzy logic, transparency and data protection, disparate data analytics, and scalability of the big data domain - Covers many applications of big data analysis in diverse fields such as chemistry, chemoinformatics, bioinformatics, computer-assisted drug/vaccine design, characterization of emerging pathogens, and environmental protection - Highlights the considerable benefits offered by big data analytics to science, in biomedical fields and in industry

Environmental Nanotoxicology: Combatting the Minute Contaminants is a comprehensive guide to the rapidly evolving field of nanotoxicology and its implications for environmental health and safety. This book results from the collaborative efforts of leading experts and researchers from diverse disciplines, aiming to thoroughly understand the interactions between nanomaterials and the environment and their potential impacts on the delicate balance of our ecosystems. Nanotechnology has witnessed remarkable innovations leading to the development of nanomaterials with novel properties and applications across various industries. Alongside these innovations, concerns have arisen about the potential risks that nanomaterials may pose to the environment and living organisms. This book addresses these concerns by comprehensively exploring the field's key concepts, principles, and methodologies. It includes case studies and offers insights into developing appropriate regulatory frameworks and guidelines for the responsible use and disposal of nanomaterials. The book is a valuable resource for researchers and professionals working in nanotoxicology on the complex challenges posed by the intersection of nanomaterials and the environment. It is also an essential reference for students studying environmental science, toxicology, and nanotechnology. Addresses risk assessment and management in nanotoxicology; Explores the life cycle assessment of nanoparticles; Sheds light on emerging technologies and future directions in environmental nanotoxicology. .

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 and deep 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.

We are in the era of large-scale science. In oncology there is a huge number of data sets grouping information on cancer genomes, transcriptomes, clinical data, and more. The challenge of big data in cancer is to integrate all this diversity of data collected into a unique platform that can be analyzed, leading to the generation of readable files. The possibility of harnessing information from all the accumulated data leads to an improvement in cancer patient treatment and outcome. Solving the big data problem in oncology has multiple facets. Big data in Oncology: Impact, Challenges, and Risk Assessment brings together insights from emerging sophisticated information and communication technologies such as artificial intelligence, data science, and big data analytics for cancer management. This book focuses on targeted disease treatment using big data analytics. It provides information about targeted treatment in oncology, challenges and application of big data in cancer therapy. Recent developments in the fields of artificial intelligence, machine learning, medical imaging, personalized medicine, computing and data analytics for improved patient care. Description of the application of big data with AI to discover new targeting points for cancer treatment. Summary of several risk assessments in the field of oncology using big data. Focus on prediction of doses in oncology using big data The most targeted or relevant audience is academics, research scholars, health care professionals, hospital management, pharmaceutical chemists, the biomedical industry, software engineers and IT professionals.

Das Buch Chemometrics and Cheminformatics in Aquatic Toxicology befasst sich mit den bestehenden und neu auftretenden Problemen der Verschmutzung der aquatischen Umwelt durch verschiedene metallische und organische Schadstoffe, insbesondere Industriechemikalien, Pharmazeutika, Kosmetika, Biozide, Nanomaterialien, Pestizide, Tenside, Farbstoffe und viele weitere. Es werden verschiedene chemometrische und cheminformatische Instrumente für Laien beschrieben mitsamt ihrer Anwendung auf die Analyse und Modellierung der Toxizitätsdaten von Chemikalien in Bezug auf unterschiedliche aquatische Organismen. Eine Reihe von Datenbanken zur aquatischen Toxizität sowie chemometrische Softwaretools und Webserver werden vorgestellt und praktische Beispiele für die Modellentwicklung gegeben, einschließlich der entsprechenden Abbildungen. Darüber hinaus enthält das Werk Fallstudien und Literaturberichte, um das Verständnis des Themas abzurunden. Außerdem lernen die Leserinnen und Leser Werkzeuge und Protokolle wie maschinelles Lernen, Data Mining sowie Methoden des QSAR-basierten und ligandenbasierten chemischen Designs kennen. Darüber hinaus bietet das Werk: * Eine umfassende Einführung in chemometrische und cheminformatische Instrumente und Techniken, insbesondere maschinelles Lernen und Data Mining * Eine Darstellung von Datenbanken zur aquatischen Toxizität, chemometrischen Softwaretools und Webservern * Praktische Beispiele und Fallstudien zur Verdeutlichung und Veranschaulichung der im Buch enthaltenen Konzepte * Eine kompakte Erläuterung der chemometrischen und cheminformatischen Instrumente sowie ihrer Anwendung auf die Analyse und Modellierung von Toxizitätsdaten Chemometrics and Cheminformatics in Aquatic Toxicology ist ideal für Forschende und Studierende der Chemie sowie der Umwelt- und Pharmawissenschaften und sollte auch in den Bibliotheken von Fachleuten in der chemischen Industrie sowie Aufsichtsbehörden, die sich mit Chemometrie beschäftigen, einen Platz finden.