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Big Data in Predictive Toxicology [Hardback]

Edited by (European Commission - Joint Research Centre, Italy), Edited by (University of Bradford, UK)
  • Formāts: Hardback, 414 pages, height x width: 234x156 mm, weight: 794 g, No
  • Sērija : Issues in Toxicology Volume 41
  • Izdošanas datums: 10-Dec-2019
  • Izdevniecība: Royal Society of Chemistry
  • ISBN-10: 1782622985
  • ISBN-13: 9781782622987
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Big Data in Predictive ToxicologyPalielināt
  • Formāts: Hardback, 414 pages, height x width: 234x156 mm, weight: 794 g, No
  • Sērija : Issues in Toxicology Volume 41
  • Izdošanas datums: 10-Dec-2019
  • Izdevniecība: Royal Society of Chemistry
  • ISBN-10: 1782622985
  • ISBN-13: 9781782622987
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781782622987.html
Keywords:
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.
Chapter 1 Big Data in Predictive Toxicology: Challenges, Opportunities and Perspectives 1(37)
Andrea-Nicole Richarz
1.1 Introduction
1(1)
1.2 Big Data in the Area of Predictive Toxicology
2(1)
1.3 The Big Vs of Predictive Toxicology Data
3(3)
1.4 Challenges of Big Data in Predictive Toxicology
6(9)
1.4.1 Need for an Adequate Infrastructure
7(2)
1.4.2 Standardisation and Data Curation
9(2)
1.4.3 Too Big? Identifying the Relevant Data
11(1)
1.4.4 Data Integration Infrastructures
11(3)
1.4.5 Making Sense of the Data
14(1)
1.5 Opportunities Provided by Big Data for Predictive Toxicology
15(10)
1.5.1 More is More - Benefits of a Broader and More Diverse Data Basis
16(2)
1.5.2 Big Data for the Big Picture
18(1)
1.5.3 Creating and Using New Knowledge: Applications for Hot Topics in (Predictive) Toxicology
19(6)
1.6 Conclusions and Perspectives
25(2)
References
27(11)
Chapter 2 Biological Data in the Light of Toxicological Risk Assessment 38(31)
Vessela Vitcheva
2.1 Introduction
38(1)
2.2 Data Generated by In Vivo Toxicity Testing
39(9)
2.2.1 Acute Toxicity Testing
40(2)
2.2.2 Toxicity Testing after Repeated Dose Administration (Short-term, Sub-chronic and Chronic Toxicity Tests)
42(2)
2.2.3 Toxicity Tests for Specific Endpoints
44(4)
2.3 Data Generated by In Vitro Studies
48(4)
2.3.1 In Vitro Test Methods for Genotoxicity/ Mutagenicity Testing
48(3)
2.3.2 In Vitro Test Methods for Carcinogenicity
51(1)
2.3.3 In Vitro and In Chemico Test Methods for Skin Sensitisation
51(1)
2.4 Mechanistic Understanding - Adverse Outcome Pathways
52(1)
2.5 Data from New Approach Methodologies
53(1)
2.6 Toxicokinetic Data
54(1)
2.7 Sources of Toxicological Data
55(4)
2.7.1 TOXicology Data NETwork (TOXNET)
55(1)
2.7.2 Registry of Toxic Effects of Chemical Substances (RTECS)
56(1)
2.7.3 Kyoto Encyclopedia of Genes and Genomes (KEGG)
56(1)
2.7.4 Aggregated Computational Toxicology Resource (ACTOR)
57(1)
2.7.5 European Chemicals Agency (ECHA) Data
57(1)
2.7.6 COSMOS Next Generation
57(1)
2.7.7 Data from Assessment Reports
58(1)
2.8 Quality of Data
59(2)
2.9 Data Interpretation
61(1)
2.10 Use of Biological Data for Predictive Toxicology
62(1)
References
63(6)
Chapter 3 Chemoinformatics Representation of Chemical Structures - A Milestone for Successful Big Data Modelling in Predictive Toxicology 69(39)
Nikolay Kochev
Nina Jeliazkova
Ivanka Tsakovska
3.1 Introduction to Chemoinformatics Approaches for Chemical Structure Representation
69(5)
3.2 Structure Representation Characteristics
74(3)
3.3 Constitutional Representations
77(1)
3.4 Topological Representations
78(3)
3.5 Linear Notations
81(4)
3.5.1 SMILES Linear Notation
82(2)
3.5.2 InChI Linear Notation
84(1)
3.5.3 SMARTS Linear Notation
84(1)
3.6 3D and 4D Structure Representations
85(6)
3.6.1 3D Structure Optimisation
87(2)
3.6.2 Conformational Search
89(2)
3.7 Molecular Descriptors, Fingerprints and Hash Codes
91(4)
3.8 Challenging Cases for Structure Representation
95(6)
3.8.1 Handling Aromaticity
97(1)
3.8.2 Unique Structure Representations
98(1)
3.8.3 Tautomerism
99(1)
3.8.4 Miscellaneous Challenges
100(1)
3.9 Chemical Substances
101(3)
3.10 Conclusions
104(1)
References
104(4)
Chapter 4 Organisation of Toxicological Data in Databases 108(58)
David Bower
Kevin Cross
Glenn Myatt
4.1 Introduction
108(3)
4.2 Standards for Exchange and Organising Toxicology Data
111(14)
4.2.1 SD Files
111(2)
4.2.2 ToxML (DB)
113(7)
4.2.3 SEND
120(2)
4.2.4 OECD Harmonised Templates
122(1)
4.2.5 ISA-Tab
123(2)
4.3 Databases and Data Sets of Toxicity Studies
125(8)
4.4 Process Management and Regulatory Compliance Databases
133(2)
4.5 Alternative Approaches Databases
135(3)
4.5.1 ToxBank
135(2)
4.5.2 ToxCast/DSSTox/Tox21
137(1)
4.5.3 PubChem
138(1)
4.5.4 NIEHS/CEBS
138(1)
4.6 Integrated Approaches
138(6)
4.7 Conclusions
144(1)
Appendix
145(15)
References
160(6)
Chapter 5 Making Big Data Available: Integrating Technologies for Toxicology Applications 166(19)
Nina Jeliazkova
Vedrin Jeliazkov
5.1 Introduction
166(2)
5.2 Data Integration Approaches
168(3)
5.3 Ontologies and Shared Vocabulary
171(1)
5.4 Web Services and Cloud Technology
172(1)
5.5 Standards
172(2)
5.6 Integrative Data Analysis
174(1)
5.7 Data Sharing Infrastructure
174(1)
5.8 Conclusions
175(1)
References
176(9)
Chapter 6 Storing and Using Qualitative and Quantitative Structure-Activity Relationships in the Era of Toxicological and Chemical Data Expansion 185(29)
Sulev Sild
Geven Piir
Daniel Neagu
Uko Maran
6.1 Introduction
185(5)
6.2 Anatomy of Predictive Models
190(3)
6.2.1 Mathematical Representation
190(1)
6.2.2 Data Set Representation
191(1)
6.2.3 Model (and Data) Provenance and Metadata
192(1)
6.3 Model Governance
193(2)
6.4 Data and File Formats for the Representation of Predictive Models
195(9)
6.4.1 QSAR Model Reporting Format
196(2)
6.4.2 QSAR-ML
198(1)
6.4.3 The Predictive Model Markup Language
199(2)
6.4.4 QsarDB Archive Format
201(3)
6.5 Applications and Solutions for the Storage of Predictive Models
204(4)
6.5.1 Repository Approaches
205(1)
6.5.2 Integrated Modelling Environment Approaches
206(2)
6.6 Conclusions
208(1)
References
209(5)
Chapter 7 Toxicogenomics and Toxicoinformatics: Supporting Systems Biology in the Big Data Era 214(28)
Terezinha M. Souza
Jos C.S. Kleinjans
Danyel G.J Jennen
7.1 Introduction to Toxicogenomics and Toxicoinformatics
214(2)
7.2 Current 'Omics Technologies
216(4)
7.2.1 Transcriptomics
216(1)
7.2.2 (Epi)Genomics
217(1)
7.2.3 Proteomics
218(1)
7.2.4 Metabolomics
219(1)
7.3 Data Handling
220(6)
7.3.1 Toxicoinformatics: Standardisation, Storage and Availability of 'Omics Data
220(3)
7.3.2 From Bits to Annotation
223(3)
7.4 Use of 'Omics Data in Predictive Toxicology
226(3)
7.4.1 Comparative Assessments
226(1)
7.4.2 Unsupervised and Supervised Pattern Recognition
226(1)
7.4.3 Connectivity Mapping (CMap)
227(1)
7.4.4 Mechanistic Analysis
228(1)
7.5 Big ('Omics) Data
229(3)
7.6 Toxicogenomics in the Big Data Era: Challenges, Perspectives and Opportunities
232(2)
References
234(8)
Chapter 8 Profiling the Tox21 Chemical Library for Environmental Hazards: Applications in Prioritisation, Predictive Modelling, and Mechanism of Toxicity Characterisation 242(22)
S. Sakamuru
H. Zhu
M. Xia
A. Simeonov
R. Huang
8.1 Introduction
242(1)
8.2 The Tox21 Process
243(8)
8.2.1 Tox21 Compound Collection
243(4)
8.2.2 Tox21 Robotic Platform
247(1)
8.2.3 Tox21 Screening Process
247(1)
8.2.4 Tox21 Data Analysis Process Including Data Quality Control
248(3)
8.3 Using the Tox21 Big Data Collection for Predictive Modelling
251(6)
8.3.1 Profiling for Compound Mechanism of Toxicity
251(1)
8.3.2 Modelling Tox21 Data for In Vivo Toxicity Prediction
252(5)
8.4 The Tox21 Data Challenge - New Methods for Data Modelling in the Big Data Era
257(2)
8.5 Conclusion
259(1)
Acknowledgements
259(1)
References
260(4)
Chapter 9 Big Data Integration and Inference 264(43)
Karen H. Watanabe-Sailor
Hristo Aladjov
Shannon M. Bell
Lyle Burgoon
Wan-Yun Cheng
Rory Conolly
Stephen W. Edwards
Natalia Garcia-Reyero
Michael L. Mayo
Anthony Schroeder
Clemens Wittwehr
Edward J. Perkins
9.1 Introduction: New Toxicology Paradigm and Challenges of Big Data Integration and Application
265(3)
9.2 Structuring Knowledge to Support Data Mining, Modelling, and Decision-making
268(6)
9.2.1 Development of a Globally Accessible Knowledge Base for Data Integration
268(1)
9.2.2 Use of Structured Knowledge
269(4)
9.2.3 Supporting Decision-making
273(1)
9.3 Integration of High-throughput Screening Assays and Curated Databases for Understanding Chemical-Bioactivity Relationships
274(5)
9.3.1 Data Generated from HTS Assays
274(1)
9.3.2 Publicly Available Databases
275(3)
9.3.3 Current Approaches for Integrating HTS Data and Databases and Computational Modelling to Predict In Vivo Toxicity From In Vitro Data
278(1)
9.4 Systems Biology: De Novo Network Inference of Chemical, Gene, Protein, and Metabolite Relationships Underlying Toxicity
279(4)
9.4.1 Network Inference
280(1)
9.4.2 Steady-state Networks
280(1)
9.4.3 Dynamic Networks
281(1)
9.4.4 Networks and Inference of Toxicological Effects
282(1)
9.5 Computationally Predicted and Putative AOP Generation
283(4)
9.5.1 Computational AOP Generation
284(3)
9.5.2 From Computationally Predicted AOPs to Putative AOPs
287(1)
9.6 Use of AOPs to Support Testing and Risk Assessment
287(3)
9.6.1 Identification of Sufficient Key Events in AOP Networks
287(2)
9.6.2 Case Study: Steatosis
289(1)
9.7 Translating Experimental Data into Model Structure: Integration of High-throughput Datasets or Expert Elicitation?
290(5)
9.7.1 Mechanistic Modelling Methods
291(1)
9.7.2 Case Study: Aromatase Inhibition Model Development
292(3)
9.8 Conclusions
295(1)
9.9 Future Directions
296(1)
References
297(10)
Chapter 10 Chemometrical Analysis of Proteomics Data 307(24)
Marjan Vracko
10.1 Introduction
307(3)
10.2 Chemometrical Methods for Data Analysis
310(5)
10.2.1 Measure of Similarity
310(1)
10.2.2 Clustering and Classification
311(2)
10.2.3 Genetic Algorithm
313(1)
10.2.4 Chemical Descriptors and Bio-descriptors
313(2)
10.3 Application Examples of Chemometrical Analyses of Proteomics Data
315(11)
10.3.1 Clustering and Classification of Proteomics Data
315(2)
10.3.2 Genetic Algorithm Applied to Proteomics Data
317(3)
10.3.3 Proteomics as Bio-descriptors
320(4)
10.3.4 Biomarkers
324(2)
10.4 Conclusions
326(1)
References
326(5)
Chapter 11 Big Data and Biokinetics 331(28)
Miyoung Yoon
Gina Song Harvey Clewell
Bas Blaauboer
11.1 Introduction
331(2)
11.2 Simple Biokinetic Modelling Approaches for Supporting the Use for Large Data Amounts
333(9)
11.2.1 Biokinetic Approach for Supporting High- throughput Testing
333(3)
11.2.2 Biokinetic Approach for Predicting Acute Toxicity
336(3)
11.2.3 Biokinetics Supporting Chemical Grouping
339(1)
11.2.4 Minimalist Biokinetics Approach
340(2)
11.3 Big Data Facilitating the Parameterisation of Biokinetic Modelling to Support the Use of Big Data for Risk Assessment
342(6)
11.3.1 Example of an Approach for Rapid PBPK Modelling: PLETHEM
343(1)
11.3.2 Building In silico Humans
344(2)
11.3.3 Obtaining Chemical Specific Parameters
346(2)
11.4 Conclusion
348(1)
List of Abbreviations
348(1)
Appendix: Examples of Life Stage Physiological Parameters for Human Populations
349(1)
Height
349(1)
Weight
350(1)
Linking the Height and Weight Growth Curves
351(1)
Body Fat Volume
352(1)
References
353(6)
Chapter 12 Role of Toxicological Big Data to Support Read-across for the Assessment of Chemicals 359(26)
Mark T.D. Cronin
Andrea-Nicole Richarz
12.1 Introduction
359(5)
12.2 Role of Toxicological Big Data to Inform and Support Read-across
364(9)
12.2.1 Supporting a Read-across Justification
364(1)
12.2.2 Investigation of Toxicological Mechanisms of Action
365(1)
12.2.3 Biological Read-across
366(2)
12.2.4 The Use of Omics Technologies for Read-across
368(2)
12.2.5 Making Sense of Big Data Compilations to Form the Bridge With Chemical Structure
370(2)
12.2.6 Read-across for Nanomaterials
372(1)
12.3 The Future - Needs for the Use of Big Data for Read-across
373(1)
12.4 Conclusions
374(1)
References
375(10)
Subject Index 385
Daniel Neagu is Professor of Computing with the University of Bradford, where he leads the Artificial Intelligence Research Group. Daniel is Fellow of the Higher Education Academy, and also member of the Institute of Electrical and Electronics Engineers: Computer Society and Computational Intelligence Society, the Association for Computing Machinery and the British Computer Society. Daniel Neagu studies the integration of explicit and implicit knowledge with means of computational intelligence. Daniel Neagu's research on machine learning, data mining, data quality and applications to product safety, predictive toxicology and engineering analytics has been published in more than 100 peer-reviewed conferences and journals, and funded by national and international research councils, organisations and industry. 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.