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Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology

Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology

Dimosthenis A. Sarigiannis, Alberto Gotti, Evangelos Handakas, Spyros P. Karakitsios
Copyright: © 2018 |Pages: 43
ISBN13: 9781522526070|ISBN10: 1522526072|EISBN13: 9781522526087
DOI: 10.4018/978-1-5225-2607-0.ch007
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MLA

Sarigiannis, Dimosthenis A., et al. "Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology." Applying Big Data Analytics in Bioinformatics and Medicine, edited by Miltiadis D. Lytras and Paraskevi Papadopoulou, IGI Global, 2018, pp. 145-187. https://doi.org/10.4018/978-1-5225-2607-0.ch007

APA

Sarigiannis, D. A., Gotti, A., Handakas, E., & Karakitsios, S. P. (2018). Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology. In M. Lytras & P. Papadopoulou (Eds.), Applying Big Data Analytics in Bioinformatics and Medicine (pp. 145-187). IGI Global. https://doi.org/10.4018/978-1-5225-2607-0.ch007

Chicago

Sarigiannis, Dimosthenis A., et al. "Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology." In Applying Big Data Analytics in Bioinformatics and Medicine, edited by Miltiadis D. Lytras and Paraskevi Papadopoulou, 145-187. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2607-0.ch007

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Abstract

This chapter aims at outlining the current state of science in the field of computational exposure biology and in particular at demonstrating how the bioinformatics techniques and algorithms can be used to support the association between environmental exposures and human health and the deciphering of the molecular and metabolic pathways of induced toxicity related to environmental chemical stressors. Examples of the integrated bioinformatics analyses outlined herein are given concerning exposure to airborne chemical mixtures, to organic compounds frequently found in consumer goods, and to mixtures of organic chemicals and metals through multiple exposure pathways. Advanced bioinformatics are coupled with big data analytics to perform studies of exposome-wide associations with putative adverse health outcomes. In conclusion, the chapter gives the reader an outline of the available computational tools and paves the way towards the development of future comprehensive applications that are expected to support efficiently exposome research in the 21st century.

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