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Author: Daniel Neagu Publisher: Royal Society of Chemistry ISBN: 1839160829 Category : Medical Languages : en Pages : 289
Book Description
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.
Author: Daniel Neagu Publisher: Royal Society of Chemistry ISBN: 1839160829 Category : Medical Languages : en Pages : 289
Book Description
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.
Author: Christoph Helma Publisher: CRC Press ISBN: 0849350352 Category : Medical Languages : en Pages : 520
Book Description
A comprehensive overview of techniques and systems currently utilized in predictive toxicology, this reference presents an in-depth survey of strategies to characterize chemical structures and biological systems—covering prediction methods and algorithms, sources of high-quality toxicity data, the most important commercial and noncommercial predictive toxicology programs, and advanced technologies in computational chemistry and biology, statistics, and data mining.
Author: Sean Ekins Publisher: John Wiley & Sons ISBN: 111928256X Category : Science Languages : de Pages : 450
Book Description
A key resource for toxicologists across a broad spectrum of fields, this book offers a comprehensive analysis of molecular modelling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals. Provides a perspective of what is currently achievable with computational toxicology and a view to future developments Helps readers overcome questions of data sources, curation, treatment, and how to model / interpret critical endpoints that support 21st century hazard assessment Assembles cutting-edge concepts and leading authors into a unique and powerful single-source reference Includes in-depth looks at QSAR models, physicochemical drug properties, structure-based drug targeting, chemical mixture assessments, and environmental modeling Features coverage about consumer product safety assessment and chemical defense along with chapters on open source toxicology and big data
Author: Subhash C. Basak Publisher: Elsevier ISBN: 0323857140 Category : Science Languages : en Pages : 503
Book Description
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
Author: Christoph Helma Publisher: CRC Press ISBN: 9780824723972 Category : Medical Languages : en Pages : 520
Book Description
A comprehensive overview of techniques and systems currently utilized in predictive toxicology, this reference presents an in-depth survey of strategies to characterize chemical structures and biological systems—covering prediction methods and algorithms, sources of high-quality toxicity data, the most important commercial and noncommercial predictive toxicology programs, and advanced technologies in computational chemistry and biology, statistics, and data mining.
Author: Patrick Omoregie Isibor Publisher: Springer Nature ISBN: 3031541545 Category : Electronic books Languages : en Pages : 358
Book Description
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. .
Author: Huixiao Hong Publisher: Springer Nature ISBN: 3031207300 Category : Medical Languages : en Pages : 654
Book Description
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.
Author: Sean Ekins Publisher: Wiley-Interscience ISBN: 9780470049624 Category : Science Languages : en Pages : 0
Book Description
A comprehensive analysis of state-of-the-art molecular modeling approaches and strategies applied to risk assessment for pharmaceutical and environmental chemicals This unique volume describes how the interaction of molecules with toxicologically relevant targets can be predicted using computer-based tools utilizing X-ray crystal structures or homology, receptor, pharmacophore, and quantitative structure activity relationship (QSAR) models of human proteins. It covers the in vitro models used, newer technologies, and regulatory aspects. The book offers a complete systems perspective to risk assessment prediction, discussing experimental and computational approaches in detail, with: * An introduction to toxicology methods and an explanation of computational methods * In-depth reviews of QSAR methods applied to enzymes, transporters, nuclear receptors, and ion channels * Sections on applying computers to toxicology assessment in the pharmaceutical industry and in the environmental arena * Chapters written by leading international experts * Figures that illustrate computational models and references for further information This is a key resource for toxicologists and scientists in the pharmaceutical industry and environmental sciences as well as researchers involved in ADMET, drug discovery, and technology and software development.
Author: Kunal Roy Publisher: John Wiley & Sons ISBN: 1119681596 Category : Science Languages : de Pages : 596
Book Description
CHEMOMETRICS AND CHEMINFORMATICS IN AQUATIC TOXICOLOGY Explore chemometric and cheminformatic techniques and tools in aquatic toxicology Chemometrics and Cheminformatics in Aquatic Toxicology delivers an exploration of the existing and emerging problems of contamination of the aquatic environment through various metal and organic pollutants, including industrial chemicals, pharmaceuticals, cosmetics, biocides, nanomaterials, pesticides, surfactants, dyes, and more. The book discusses different chemometric and cheminformatic tools for non-experts and their application to the analysis and modeling of toxicity data of chemicals to various aquatic organisms. You’ll learn about a variety of aquatic toxicity databases and chemometric software tools and webservers as well as practical examples of model development, including illustrations. You’ll also find case studies and literature reports to round out your understanding of the subject. Finally, you’ll learn about tools and protocols including machine learning, data mining, and QSAR and ligand-based chemical design methods. Readers will also benefit from the inclusion of: A thorough introduction to chemometric and cheminformatic tools and techniques, including machine learning and data mining An exploration of aquatic toxicity databases, chemometric software tools, and webservers Practical examples and case studies to highlight and illustrate the concepts contained within the book A concise treatment of chemometric and cheminformatic tools and their application to the analysis and modeling of toxicity data Perfect for researchers and students in chemistry and the environmental and pharmaceutical sciences, Chemometrics and Cheminformatics in Aquatic Toxicology will also earn a place in the libraries of professionals in the chemical industry and regulators whose work involves chemometrics.
Author: Neeraj Kumar Fuloria Publisher: CRC Press ISBN: 1000965260 Category : Medical Languages : en Pages : 415
Book Description
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.
Author: Daniel Neagu
Author: Daniel Neagu
Author: Christoph Helma
Author: Sean Ekins
Author: Subhash C. Basak
Author: Christoph Helma
Author: Patrick Omoregie Isibor
Author: Huixiao Hong
Author: Sean Ekins
Author: Kunal Roy
Author: Neeraj Kumar Fuloria