A Large Language Model-Powered Map of Metabolomics Research.

ABSTRACT: We present a comprehensive map of the metabolomics research landscape, synthesizing insights from over 80,000 publications. Using PubMedBERT, we transformed abstracts into 768-dimensional embeddings that capture the nuanced thematic structure of the field. Dimensionality reduction with t-SNE revealed distinct clusters corresponding to key domains such as analytical chemistry, plant biology, pharmacology, and clinical diagnostics. In addition, a neural topic modeling pipeline refined with GPT-4o mini reclassified the corpus into 20 distinct topics-ranging from "Plant Stress Response Mechanisms" and "NMR Spectroscopy Innovations" to "COVID-19 Metabolomic and Immune Responses." Temporal analyses further highlight trends including the rise of deep learning methods post-2015 and a continued foc

SUBMITTER: Bifarin OO 

PROVIDER: S-EPMC11957067 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature