ArticleAnalytical chemistry2022
Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics.
Article in Analytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Mass Spectrometry-Based Chromatographic and Computational Workflows for Biomarker and Therapeutic Target Discovery: A Comprehensive Review.Biomedical chromatography : BMC · 2026Review
- Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.Analytical chemistry · 2026Article
- Leveraging 13C-Labeling to Assign Molecular Formulas to Unknown Yeast Metabolites.Journal of the American Society for Mass Spectrometry · 2026Article
- Decoding cancer across scales with metabolomics.Nature reviews. Cancer · 2026Review
- CellMate─A Deep Learning-Assisted Single-Cell Data Processing Platform.Analytical chemistry · 2026Article
- Current Advancements in Untargeted Metabolomics Analysis and Testing Driven by Machine Learning: Prospects for Artificial Intelligence in Patient-Centric Healthcare Transformation.Applied biochemistry and biotechnology · 2026Review
- Machine Learning-Assisted False Positive Detection in Metabolite Identification Workflows.Analytical chemistry · 2025Article
- Review
- Machine Learning for Enhanced Identification Probability in RPLC/HRMS Nontargeted Workflows.Analytical chemistry · 2025Article
- End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.Analytical chemistry · 2025Article
- Techniques, Databases and Software Used for Studying Polar Metabolites and Lipids of Gastrointestinal Parasites.Animals : an open access journal from MDPI · 2024Review
- Prioritization, Identification, and Quantification of Emerging Contaminants in Recycled Textiles Using Non-Targeted and Suspect Screening Workflows by LC-ESI-HRMS.Analytical chemistry · 2024Article
- Computational methods for processing and interpreting mass spectrometry-based metabolomics.Essays in biochemistry · 2024Review
- Neuroactive metabolites and bile acids are altered in extremely premature infants with brain injury.Cell reports. Medicine · 2024Article
- Clinical advances in analytical profiling of signature lipids: implications for severe non-communicable and neurodegenerative diseases.Metabolomics : Official journal of the Metabolomic Society · 2024Review
- Microbiome metabolite quantification methods enabling insights into human health and disease.Methods (San Diego, Calif.) · 2024Review
- Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics.BMC bioinformatics · 2023Article
- HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks.Analytical chemistry · 2023Article
- PeakDetective: A Semisupervised Deep Learning-Based Approach for Peak Curation in Untargeted Metabolomics.Analytical chemistry · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS, which uses machine learning based on a convoluted neural network to reduce the number and fraction of false peaks. NeatMS comes with a pre-trained model representing expert knowledge in the differentiation of true chemical signal from noise. Furthermore, it provides all necessary functions to easily train new models or improve existing ones by transfer learning. Thus, the tool improves peak curation and contributes to the robust and scalable analysis of large-scale experiments. We show how to integrate it into different liquid chromatography-mass spectrometry (LC-MS) analysis workflows, quantify its performance, and compare it to various other approaches. NeatMS software is available as open source on github under permissive MIT license and is also provided as easy-to-install PyPi and Bioconda packages.
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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.