ArticleNature communications2024
An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Computational Mass Spectrometry Imaging in the Era of AI.Chemical reviews · 2026Review
- From known chemical space to unannotated metabolites: a cluster-guided retention-time driven framework for biologically informed annotation.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets.Analytical chemistry · 2026Article
- Self-Powered Flexible Triboelectric-Gated Ion-Gel Transistor for Neuromorphic Tactile Sensing and Human Activity Recognition.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- mFLIP: metabolic flux interval prediction.BMC bioinformatics · 2026Article
- Physiological and Proteomic Insights into Melatonin-Mediated Regulation of Copper Toxicity in the CrayfishInternational journal of molecular sciences · 2026Article
- ML-BUSMetab: Machine Learning-Based Metabolomic Profiling for Predicting Aspirin Response in Colorectal Cancer Chemoprevention: A Multi-Model Explainable Artificial Intelligence Approach with External Validation.Journal of clinical medicine · 2026Article
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Lightweight liquid neural networks decipher salivary metabolic fingerprinting for high-risk periodontitis screening in diabetes.NPJ digital medicine · 2026Article
- LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening.Analytical chemistry · 2026Article
- CsdA-LaeB Regulatory Hub Contributes to Aspergillus fumigatus Virulence via Fumiquinazoline C Biosynthesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Urinary metabolomics-based machine learning for diagnosis of early gastric neoplasia: a retrospective diagnostic case-control study.Frontiers in oncology · 2026Article
- Recent advances in the application of artificial intelligence and wearable devices in volleyball.Frontiers in sports and active living · 2026Review
- Metabolic interactions in the brain: the crucial roles of neurons, astrocytes, and microglia in health and disease.Frontiers in neuroscience · 2026Review
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- Non-Targeted Metabolomics Analysis of Metabolic Differences Between Different Concentrations of Protein Diets in the Longest Dorsal Muscle of Tibetan Pigs.Metabolites · 2025Article
- Comparative analysis of continuous similarity measures for compound identification in mass spectrometry-based metabolomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2025Article
- Infrared spectroscopy-based zero-shot learning for identifying reaction intermediates in unseen systems.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The Deep Mining Era: Genomic, Metabolomic, and Integrative Approaches to Microbial Natural Products from 2018 to 2024.Marine drugs · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.
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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.