Exposome data analysis toolbox
This WP aims to develop a set of advanced statistical and bioinformatics analysis strategies, tools, codes and tutorials for the analysis of exposome data. This includes:
- The analysis of longitudinal exposome and health associations, including strategies aimed at risk prediction such as machine-learning (black box) techniques, and those aimed at estimating interpretable causal parameters for relevant exposures
- The analysis of combined exposures effects
- The integration of exposome and cross-omics data
- Strategies for incorporating a-priori knowledge on causal structures and mediators to improve causal inference
- The development of DataSHIELD R packages to perform federated non-disclosive analyses of the exposome
- The development of open access tools, tutorials, courses and e-learning material
Oliver Robinson, Xavier Basagaña, Lydiane Agier, Montserrat de Castro, Carles Hernandez-Ferrer, Juan R. Gonzalez, Joan O. Grimal, Mark Nieuwenhuijsen, Jordi Sunyer, Rémy Slama, and Martine Vrijheid. The Pregnancy Exposome: Multiple Environmental Exposures in the INMA-Sabadell Birth Cohort. Environ. Sci. Technol. July, 2015. DOI: 10.1021/acs.est.5b01782.
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