Learning High-Dimensional Mechanistic Pathways of Exposome to Health Outcomes using Mixed Integer Optimization Algorithms

Music City Center 

This talk will focus on a new approach to studying high-dimensional mechanistic pathways of exposome to health outcomes in the framework of homogeneity pursuit (HP). HP allows scientists to cluster similar toxicants into mixtures while accommodating high-dimensional mediators (e.g. metabolites) that play different roles in mediating the relationships between mixtures and health outcomes. Statistical learning is built upon integer optimization algorithms that formulate the task on clustering of toxicants into an estimation problem. Moreover, we propose an ensemble inference that can provide confidence intervals for high-dimensional direct and indirect effects. This new statistical toolbox will be illustrated by simulation studies and real-world data examples.

Exposome

Directed acyclic graph

Constrained optimization