Data-driven Metabolite Annotation
Data-Driven Metabolite Annotation is a computational approach within metabolomics that systematically identifies and characterizes metabolites in complex biological samples by leveraging empirical data patterns and statistical inference, distinguished from hypothesis-driven methods through its reliance on high-throughput experimental data and machine-learning or pattern-recognition algorithms to assign putative chemical identities.
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10.4K
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592
Authors
267
Institutions
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2017–2026
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HMDB: a knowledgebase for the human metabolome
David S. Wishart, Craig Knox, An Chi Guo et al. · Nucleic Acids Research · 2008 · 1.9K citations · Full text
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University of California San Diego
San Diego, United States
7 h-index
11 publications
483 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
21 publications
2.2K citations
Leibniz Institute of Plant Biochemistry
Halle, Germany
5 h-index
10 publications
4.4K citations
University of Geneva
Geneva, Switzerland
4 h-index
12 publications
1.4K citations
Wageningen University & Research
Wageningen, The Netherlands
4 h-index
11 publications
955 citations
University of Chinese Academy of Sciences
Beijing, China
4 h-index
14 publications
811 citations

Fondazione Edmund Mach
San Michele all'Adige, Italy
4 h-index
10 publications
538 citations
European Molecular Biology Laboratory
Hamburg, Germany
4 h-index
29 publications
1.7K citations
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12 publications · 2.4K citations
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9 publications · 592 citations
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4 publications · 221 citations
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4 publications · 130 citations
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4 publications · 496 citations
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