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Informatics and Data Analytics to Support Exposome-Based Discovery: Part 2 - Computational Exposure Biology
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Author(s): Dimosthenis A. Sarigiannis (Aristotle University of Thessaloniki, Greece), Alberto Gotti (Aristotle University of Thessaloniki, Greece), Evangelos Handakas (Aristotle University of Thessaloniki, Greece) and Spyros P. Karakitsios (Aristotle University of Thessaloniki, Greece)
Copyright: 2018
Pages: 43
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.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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