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Informatics and Data Analytics to Support Exposome-Based Discovery: Part 1 - Assessment of External and Internal Exposure

Informatics and Data Analytics to Support Exposome-Based Discovery: Part 1 - Assessment of External and Internal Exposure

Dimosthenis A. Sarigiannis, Spyros P. Karakitsios, Evangelos Handakas, Krystalia Papadaki, Dimitris Chapizanis, Alberto Gotti
Copyright: © 2018 |Pages: 30
ISBN13: 9781522526070|ISBN10: 1522526072|EISBN13: 9781522526087
DOI: 10.4018/978-1-5225-2607-0.ch006
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MLA

Sarigiannis, Dimosthenis A., et al. "Informatics and Data Analytics to Support Exposome-Based Discovery: Part 1 - Assessment of External and Internal Exposure." Applying Big Data Analytics in Bioinformatics and Medicine, edited by Miltiadis D. Lytras and Paraskevi Papadopoulou, IGI Global, 2018, pp. 115-144. https://doi.org/10.4018/978-1-5225-2607-0.ch006

APA

Sarigiannis, D. A., Karakitsios, S. P., Handakas, E., Papadaki, K., Chapizanis, D., & Gotti, A. (2018). Informatics and Data Analytics to Support Exposome-Based Discovery: Part 1 - Assessment of External and Internal Exposure. In M. Lytras & P. Papadopoulou (Eds.), Applying Big Data Analytics in Bioinformatics and Medicine (pp. 115-144). IGI Global. https://doi.org/10.4018/978-1-5225-2607-0.ch006

Chicago

Sarigiannis, Dimosthenis A., et al. "Informatics and Data Analytics to Support Exposome-Based Discovery: Part 1 - Assessment of External and Internal Exposure." In Applying Big Data Analytics in Bioinformatics and Medicine, edited by Miltiadis D. Lytras and Paraskevi Papadopoulou, 115-144. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2607-0.ch006

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Abstract

This chapter provides a comprehensive overview of the state of the art and beyond regarding modelling and data analytics towards refined external and internal exposure assessment, for elucidating the human exposome. This includes methods for more accurate measurement of personal exposure (using wearable sensors) and for extrapolation to larger population groups (agent-based modelling). A key component in the modern risk and health impact assessment is the translation of external exposure into internal exposure metrics, accounting for age, gender, genetic and route of exposure dependent differences. The applicability of biokinetics covering a large chemical space is enhanced using quantitative structure activity relationships, especially when the latter are estimated using machine learning tools. Finally, comprehensive biomonitoring data interpretation and assimilation are supported by exposure reconstruction algorithms coupled with biokinetics

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