CHAPTER 9: Big Data Integration and Inference

Karen H. Watanabe-Sailor; Hristo Aladjov; Shannon M. Bell; Lyle Burgoon; Wan Yun Cheng; Rory Conolly; Stephen W. Edwards; Nàtalia Garcia-Reyero; Michael L. Mayo; Anthony Schroeder; Clemens Wittwehr; Edward J. Perkins

doi:10.1039/9781782623656-00264

CHAPTER 9: Big Data Integration and Inference

Karen H. Watanabe-Sailor, Hristo Aladjov, Shannon M. Bell, Lyle Burgoon, Wan Yun Cheng, Rory Conolly, Stephen W. Edwards, Nàtalia Garcia-Reyero, Michael L. Mayo, Anthony Schroeder, Clemens Wittwehr, Edward J. Perkins

Mathematical and Natural Sciences, School of (SMNS)

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Scopus citations

Abstract

Toxicology data are generated on large scales by toxicogenomic studies and high-throughput screening (HTS) programmes, and on smaller scales by traditional methods. Both big and small data have value for elucidating toxicological mechanisms and pathways that are perturbed by chemical stressors. In addition, years of investigations comprise a wealth of knowledge as reported in the literature that is also used to interpret new data, though knowledge is not often captured in traditional databases. With the big data era, computer automation to analyse and interpret datasets is needed, which requires aggregation of data and knowledge from all available sources. This chapter reviews ongoing efforts to aggregate toxicological knowledge in a knowledge base, based on the Adverse Outcome Pathways framework, and provides examples of data integration and inferential analysis for use in (predictive) toxicology.

Original language	English (US)
Title of host publication	Big Data in Predictive Toxicology
Editors	Daniel Neagu, Andrea-Nicole Richarz
Publisher	Royal Society of Chemistry
Pages	264-306
Number of pages	43
Edition	41
DOIs	https://doi.org/10.1039/9781782623656-00264
State	Published - 2020

Publication series

Name	Issues in Toxicology
Number	41
Volume	2020-January
ISSN (Print)	1757-7179
ISSN (Electronic)	1757-7187

ASJC Scopus subject areas

Toxicology
Pharmacology
Health, Toxicology and Mutagenesis

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10.1039/9781782623656-00264

Cite this

Watanabe-Sailor, K. H., Aladjov, H., Bell, S. M., Burgoon, L., Cheng, W. Y., Conolly, R., Edwards, S. W., Garcia-Reyero, N., Mayo, M. L., Schroeder, A., Wittwehr, C., & Perkins, E. J. (2020). CHAPTER 9: Big Data Integration and Inference. In D. Neagu, & A-N. Richarz (Eds.), Big Data in Predictive Toxicology (41 ed., pp. 264-306). (Issues in Toxicology; Vol. 2020-January, No. 41). Royal Society of Chemistry. https://doi.org/10.1039/9781782623656-00264

CHAPTER 9: Big Data Integration and Inference. / Watanabe-Sailor, Karen H.; Aladjov, Hristo; Bell, Shannon M. et al.
Big Data in Predictive Toxicology. ed. / Daniel Neagu; Andrea-Nicole Richarz. 41. ed. Royal Society of Chemistry, 2020. p. 264-306 (Issues in Toxicology; Vol. 2020-January, No. 41).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Watanabe-Sailor, KH, Aladjov, H, Bell, SM, Burgoon, L, Cheng, WY, Conolly, R, Edwards, SW, Garcia-Reyero, N, Mayo, ML, Schroeder, A, Wittwehr, C & Perkins, EJ 2020, CHAPTER 9: Big Data Integration and Inference. in D Neagu & A-N Richarz (eds), Big Data in Predictive Toxicology. 41 edn, Issues in Toxicology, no. 41, vol. 2020-January, Royal Society of Chemistry, pp. 264-306. https://doi.org/10.1039/9781782623656-00264

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CHAPTER 9: Big Data Integration and Inference

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