About this book
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.
- Big Data in Predictive Toxicology - Challenges, Opportunities and Perspectives
- Biological Data in the Light of Toxicological Risk Assessment
- Chemoinformatics Representation of Chemical Structures – A Milestone for Successful Big Data Modelling in Predictive Toxicology
- Organisation of Toxicological Data in Databases
- Making Big Data Available: Integrating Technologies for Toxicology Applications
- Storing and Using Qualitative and Quantitative Structure–Activity Relationships in the Era of Toxicological and Chemical Data Expansion
- Toxicogenomics and Toxicoinformatics: Supporting Systems Biology in the Big Data Era
- Profiling the Tox21 Chemical Library for Environmental Hazards: Applications in Prioritisation, Predictive Modelling, and Mechanism of Toxicity Characterisation
- Big Data Integration and Inference
- Chemometrical Analysis of Proteomics Data
- Big Data and Biokinetics
- Role of Toxicological Big Data to Support Read-Across for the Assessment of Chemicals
The print version of this book is planned for release on 16 December 2019. Information about this book is subject to change without notice.Pre-order hardback £169.00 *
Daniel Neagu is a Professor of Computing with the University of Bradford. He concurrently leads the Artificial Intelligence Research Group. Daniel is on the Higher Education Academy register of practitioners and is a Fellow of the Higher Education Academy. Amongst other achievements Daniel is also a member of the UK Council for Graduate Education Postgraduate Funding and Resourcing Working Group, the Institute of Electrical and Electronics Engineers, the Computer Society and Computational Intelligence Society, the Association for Computing Machinery and the British Computer Society.
Dr Andrea Richarz holds a diploma and PhD in Chemistry from the Technical University of Berlin. She was then a post-doctoral researcher at the Hahn-Meitner-Institute. Andrea is now the Project Manager for the EU COSMOS project at Liverpool John Moores University. In her career she also has been the Managing Editor of the Journal of Trace Elements in Medicine and Biology, a member of the Scientific Advisory Board and Vice President of the Society for Minerals and Trace Elements. She has spent several years researching in silico alternatives to animal testing for chemical risk assessment. She also managed the international EU funded project OSIRIS on Integrated Testing Strategies for the risk assessment of industrial chemicals.