Evidence map›Paper›PMID 39903752›Full record

ArticleAnalytical chemistry2025

Molecular Structure Discovery for Untargeted Metabolomics Using Biotransformation Rules and Global Molecular Networking.

Margaret R Martin, Wout Bittremieux, Soha Hassoun

Abstract read
In one paragraph

Article in Analytical chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Margaret R MartinDepartment of Computer Science, Tufts University, Medford, Massachusetts 02155, United States.
Wout BittremieuxDepartment of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.ORCID 0000-0002-3105-1359
Soha HassounDepartment of Computer Science, Tufts University, Medford, Massachusetts 02155, United States.ORCID 0000-0001-9477-2199

Funding

Computational Techniques for Advancing Untargeted Metabolomics AnalysisR01GM132391 · NIGMS · TUFTS UNIVERSITY MEDFORD · PI HASSOUN, SOHA · 2019 to 2022
$1.5M
Deep Learning Models for Metabolomics AnalysisR35GM148219 · NIGMS · TUFTS UNIVERSITY MEDFORD · PI Soha Hassoun · 2023 to 2026
$1.4M
NIGMS NIH HHS R01 GM132391NIGMS NIH HHS R35 GM148219
6 · The paper itself

Abstract

Although untargeted mass spectrometry-based metabolomics is crucial for understanding life's molecular underpinnings, its effectiveness is hampered by low annotation rates of the generated tandem mass spectra. To address this issue, we introduce a novel data-driven approach, Biotransformation-based Annotation Method (BAM), that leverages molecular structural similarities inherent in biochemical reactions. BAM operates by applying biotransformation rules to known "anchor" molecules, which exhibit high spectral similarity to unknown spectra, thereby hypothesizing and ranking potential structures for the corresponding "suspect" molecule. BAM's effectiveness is demonstrated by its success in annotating query spectra in a global molecular network comprising hundreds of millions of spectra. BAM was able to assign correct molecular structures to 24.2% of examined anchor-suspect cases, thereby demonstrating remarkable advancement in metabolite annotation.

Indexed as

MetabolomicsBiotransformationMolecular StructureTandem Mass Spectrometry

Identifiers

PMID39903752
PMCPMC11841678

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.