ArticleProceedings of the National Academy of Sciences of the United States of America2016
Topic modeling for untargeted substructure exploration in metabolomics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers.
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Who cites it
176 citing papers in PubMed.
- eCOMET: an R package for evaluating metabolic diversity and enrichment from LC-MS/MS data to test ecological hypotheses from individuals to ecosystems.The New phytologist · 2026Article
- Molecular Networking and Mass2Motif Analysis Prioritise Glycosylated Triterpenoid-Related Chemical Space in the Cytotoxic Fractions of EdibleFoods (Basel, Switzerland) · 2026Article
- DeepMASS v.2: An enhanced deep learning platform for large-scale discovery and structural annotation of unknown plant metabolites.Plant communications · 2026Article
- Chemical Ontogeny in Plants: Developmental Dynamics of Specialized Metabolism and Implications for Ecology, Quality Control, and Bioprospecting.Plants (Basel, Switzerland) · 2026Review
- MetaboAnnotate: An AI-powered Multiagent Framework for Integrating Annotation Tools for Untargeted Metabolomics.Analytical chemistry · 2026Article
- Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.Analytical chemistry · 2026Article
- Large-scale discovery and annotation of substructure patterns in mass spectrometry profiles.Nature communications · 2026Article
- Bioactivity-guided metabolomics reveals discriminant cytotoxic signatures in Siparuna guianensis.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Comprehensive Metabolite Profile and Cytotoxic Constituents ofJournal of natural products · 2026Article
- Structural determination of natural products at speeds and scale of high-throughput screening.Natural product reports · 2026Review
- Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS.Nature biotechnology · 2026Article
- Chemical diversity and species differentiation in Brazilian Vanilla: insights from LC-HRMS/MS metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Learning from All Views: A Multiview Contrastive Framework for Metabolite Annotation.Analytical chemistry · 2026Article
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- ChemEmbed: a deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings.Briefings in bioinformatics · 2026Article
- Advances in natural product discovery: strategies, technologies, and insights.Natural products and bioprospecting · 2026Review
- Productive chaos and precision engineering: decoupling discovery from manufacturing to revolutionize plant-inspired therapeutics.Frontiers in plant science · 2026Article
- Recent Advances in Implementation of Machine Learning for Environmental Nontarget Identification.Environment & health (Washington, D.C.) · 2025Review
- Learning from All Views: A Multiview Contrastive Framework for Metabolite Annotation.bioRxiv : the preprint server for biology · 2025Article
- A large language model for deriving spectral embeddings for accurate compound identification in mass spectrometry.Communications chemistry · 2025Article
116 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures ("Mass2Motifs") from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.
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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.