Constructing Metabolic Association Networks Using High-dimensional Mass Spectrometry Data.

ABSTRACT: The goal of metabolic association networks is to identify topology of a metabolic network for a better understanding of molecular mechanisms. An accurate metabolic association network enables investigation of the functional behavior of metabolites in a cell or tissue. Gaussian Graphical model (GGM)-based methods have been widely used in genomics to infer biological networks. However, the performance of various GGM-based methods for the construction of metabolic association networks remains unknown in metabolomics. The performance of principle component regression (PCR), independent component regression (ICR), shrinkage covariance estimate (SCE), partial least squares regression (PLSR), and extrinsic similarity (ES) methods in constructing metabolic association networks was compared by esti

SUBMITTER: Koo I 

PROVIDER: S-EPMC4233464 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature