An Integrated Gaussian Graphical Model to evaluate the impact of exposures on metabolic networks.

ABSTRACT: Examining the effects of exogenous exposures on complex metabolic processes poses the unique challenge of identifying interactions among a large number of metabolites. Recent progress in the quantification of the metabolome through mass spectrometry (MS) and nuclear magnetic resonance (NMR) has given rise to high-dimensional biomedical data of specific metabolites that can be leveraged to study their effects in humans. These metabolic interactions can be evaluated using probabilistic graphical models (PGMs), which define conditional dependence and independence between components within and between heterogeneous biomedical datasets. This method allows for the detection and recovery of valuable but latent information that cannot be easily detected by other currently existing methods. Here, w

SUBMITTER: Lee JW 

PROVIDER: S-EPMC6817396 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature