Exploratory analysis of metabolic changes using mass spectrometry data and graph embeddings.

ABSTRACT: Mass spectrometry (MS)-based metabolomics analysis is a powerful tool, but it comes with its own set of challenges. The MS workflow involves multiple steps before its interpretation in what is denominate data mining. Data mining consists of a two-step process. First, the MS data is ordered, arranged, and presented for filtering before being analyzed. Second, the filtered and reduced data are analyzed using statistics to remove further variability. This holds true particularly for MS-based untargeted metabolomics studies, which focused on understanding fold changes in metabolic networks. Since the task of filtering and identifying changes from a large dataset is challenging, automated techniques for mining untargeted MS-based metabolomic data are needed. The traditional statistics-based app

SUBMITTER: Alvarez-Mamani E 

PROVIDER: S-EPMC11604959 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature