
Big Data in Predictive Toxicology
Description
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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.
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Persons
Dr Andrea Richarz holds a diploma and PhD in Chemistry from the Technical University Berlin. She has managed two large international EU research projects in the area of computational toxicology and new approaches for chemical safety assessment, related to REACH chemicals and cosmetics substances, and was also involved in nanosafety project research. As Scientific Officer at the European Commission Joint Research Centre in Ispra, Italy she worked in the area of predictive toxicology, in silico methods and read-across, with special interest in integrated chemical safety assessment approaches as well as combined exposure to chemicals, including uncertainties of and confidence in the approaches in view of their regulatory acceptance. She has recently joined the European Chemicals Agency in Helsinki.
Content
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
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