Deriving Lipid Classification Based on Molecular Formulas.

ABSTRACT: Despite instrument and algorithmic improvements, the untargeted and accurate assignment of metabolites remains an unsolved problem in metabolomics. New assignment methods such as our SMIRFE algorithm can assign elemental molecular formulas to observed spectral features in a highly untargeted manner without orthogonal information from tandem MS or chromatography. However, for many lipidomics applications, it is necessary to know at least the lipid category or class that is associated with a detected spectral feature to derive a biochemical interpretation. Our goal is to develop a method for robustly classifying elemental molecular formula assignments into lipid categories for an application to SMIRFE-generated assignments. Using a Random Forest machine learning approach, we developed a meth

SUBMITTER: Mitchell JM 

PROVIDER: S-EPMC7143220 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature