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.

85

Publications

10.4K

Citations

592

Authors

267

Institutions

Publications per year

2017–2026

53

Authors

592

  1. TA
    Theodore Alexandrov

    University of California San Diego, United States

    4 publications · 147 citations

    4

    h-index

  2. SN
    Steffen Neumann

    Leibniz Institute of Plant Biochemistry, Germany

    4 publications · 1.7K citations

    4

    h-index

  3. ZZ
    Zheng‐Jiang Zhu

    Shanghai Institute of Organic Chemistry, China

    3 publications · 429 citations

    3

    h-index

  4. DS
    David S. Wishart

    University of Alberta, Canada

    3 publications · 3.8K citations

    3

    h-index

  5. DS
    Daisuke Shibata

    Kazusa DNA Research Institute, Japan

    3 publications · 327 citations

    3

    h-index

1–5 of 592

Venues

  1. Analytical Chemistry

    12 publications · 2.4K citations

    11

    h-index

  2. Bioinformatics

    9 publications · 592 citations

    9

    h-index

  3. Metabolites

    4 publications · 221 citations

    5

    h-index

  4. Food Chemistry

    4 publications · 130 citations

    4

    h-index

  5. Nature Communications

    4 publications · 496 citations

    4

    h-index

1–5