Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics.

ABSTRACT: Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomic

SUBMITTER: Traquete F 

PROVIDER: S-EPMC9353772 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature