Published in

Asian Network for Scientific Information (ANSINET), Biotechnology, p. 744-787, 2019

DOI: 10.4018/978-1-5225-8903-7.ch030

Advances in Bioinformatics and Biomedical Engineering, p. 145-187

DOI: 10.4018/978-1-5225-2607-0.ch007

Advances in Bioinformatics and Biomedical Engineering, p. 115-144

DOI: 10.4018/978-1-5225-2607-0.ch006

Links

Tools

Export citation

Search in Google Scholar

Informatics and Data Analytics to Support Exposome-Based Discovery

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Red circle
Preprint: archiving forbidden
Red circle
Postprint: archiving forbidden
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

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.