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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
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Author(s): Dimosthenis A. Sarigiannis (Aristotle University of Thessaloniki, Greece), Spyros P. Karakitsios (Aristotle University of Thessaloniki, Greece), Evangelos Handakas (Aristotle University of Thessaloniki, Greece), Krystalia Papadaki (Aristotle University of Thessaloniki, Greece), Dimitris Chapizanis (Aristotle University of Thessaloniki, Greece) and Alberto Gotti (Aristotle University of Thessaloniki, Greece)
Copyright: 2018
Pages: 30
Source title: Applying Big Data Analytics in Bioinformatics and Medicine
Source Author(s)/Editor(s): Miltiadis D. Lytras (Deree - The American College of Greece, Greece) and Paraskevi Papadopoulou (Deree - The American College of Greece, Greece)
DOI: 10.4018/978-1-5225-2607-0.ch006



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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