ArticleNature communications2025
Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Biosynthesis-Informed Putative Annotation Reveals Alkaloid Profiles Across Tissues ofMetabolites · 2026Article
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Genome-wide association study reveals loci associated with cytokine levels in chicken serum.Poultry science · 2026Article
- Combating Antibacterial Resistance: The Integrative Role of Artificial Intelligence in Bio-Based Product Development.Antibiotics (Basel, Switzerland) · 2026Review
- Structure-informed deep generation enables de novo metabolite annotation in untargeted metabolomics.Nature communications · 2026Article
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Article
- Decoding the Metabolic Signatures of Neurodegeneration Diseases: Advances in Mass Spectrometry-Based Metabolomics.Metabolites · 2026Review
- Intermittent hypoxia drives lung microbiome-metabolome remodeling to create a pro-inflammatory landscape in murine OSAHS.Frontiers in microbiology · 2026Article
- Large-Scale Metabolite Annotation in Untargeted Metabolomics Using MetDNA.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Decoding cardiac metabolic reprogramming through single-cell multi-omics: from mechanisms to therapeutic applications.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolite annotation in untargeted metabolomics remains challenging due to the vast structural diversity of metabolites. Network-based approaches have emerged as powerful strategies, particularly for annotating metabolites lacking chemical standards. Here, we develop a two-layer interactive networking topology that integrates data-driven and knowledge-driven networks to enhance metabolite annotation. A comprehensive metabolic reaction network is curated using graph neural network-based prediction of reaction relationships, enhancing both coverage and network connectivity. Experimental data are pre-mapped onto this network via sequential MS1 matching, reaction relationship mapping, and MS2 similarity constraints. The generated networking topology enables interactive annotation propagation with over 10-fold improved computational efficiency. In common biological samples, it annotates over 1600 seed metabolites with chemical standards and >12,000 putatively annotated metabolites through network-based propagation. Notably, two previously uncharacterized endogenous metabolites absent from human metabolome databases have been discovered. Overall, this strategy significantly improves the coverage, accuracy, and efficiency of metabolite annotation and is freely available as MetDNA3.
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Registered trials
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