ArticlePloS one2020
MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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.
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.
Who cites it
21 citing papers in PubMed.
- Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles.Nature communications · 2026Article
- Tracking the Metabolites of Health and Disease Using Artificial Intelligence.Diseases (Basel, Switzerland) · 2026Review
- mineMS2: annotation of spectral libraries with exact fragmentation patterns.Journal of cheminformatics · 2025Article
- An approach of molecular-fingerprint prediction implementing a GAT.RSC advances · 2025Article
- Microbiome metabolite quantification methods enabling insights into human health and disease.Methods (San Diego, Calif.) · 2024Review
- An end-to-end deep learning framework for translating mass spectra to de-novo molecules.Communications chemistry · 2023Article
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics.Advances in experimental medicine and biology · 2023Article
- MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication.Molecules (Basel, Switzerland) · 2022Review
- Artificial intelligence in microbial natural product drug discovery: current and emerging role.Natural product reports · 2022Review
- Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- The critical role that spectral libraries play in capturing the metabolomics community knowledge.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- Integrative omics approaches for biosynthetic pathway discovery in plants.Natural product reports · 2022Review
- Article
- Metabolite discovery: Biochemistry's scientific driver.Cell metabolism · 2022Review
- Strategies for structure elucidation of small molecules based on LC-MS/MS data from complex biological samples.Computational and structural biotechnology journal · 2022Review
- Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.Natural product reports · 2021Review
- New software tools, databases, and resources in metabolomics: updates from 2020.Metabolomics : Official journal of the Metabolomic Society · 2021Review
- Seeing the forest for the trees: Retrieving plant secondary biochemical pathways from metabolome networks.Computational and structural biotechnology journal · 2021Review
- Machine Learning Applications for Mass Spectrometry-Based Metabolomics.Metabolites · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.