B0389
Title: Shifting-corrected regularized regression model for NMR metabolomic identification
Authors: Thao Vu - University of Colorado (United States) [presenting]
Abstract: The process of identifying metabolites in complex mixtures plays a critical role in metabolomic studies to obtain an informative interpretation of underlying biological processes. Manual approaches are time-consuming and heavily reliant on the knowledge and assessment of nuclear magnetic resonance (NMR) experts. We propose a shifting-corrected regularized regression method, which identifies metabolites in a mixture automatically. Using a novel weight function, the proposed method is able to detect and correct peak shifting errors caused by fluctuations in experimental procedures. Simulation studies show that the proposed method performs better with regard to the identification of metabolites in a complex mixture. We also demonstrate real data applications of our method using experimental and biological NMR mixtures.
