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An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.
- Source :
-
Nature communications [Nat Commun] 2024 Aug 20; Vol. 15 (1), pp. 7136. Date of Electronic Publication: 2024 Aug 20. - Publication Year :
- 2024
-
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.<br /> (© 2024. The Author(s).)
- Subjects :
- Humans
Adenocarcinoma of Lung metabolism
Adenocarcinoma of Lung blood
Adenocarcinoma of Lung diagnosis
Male
Female
Data Analysis
Reproducibility of Results
Middle Aged
Deep Learning
Metabolomics methods
Mass Spectrometry methods
Lung Neoplasms metabolism
Lung Neoplasms blood
Lung Neoplasms diagnosis
Metabolome
Subjects
Details
- Language :
- English
- ISSN :
- 2041-1723
- Volume :
- 15
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Nature communications
- Publication Type :
- Academic Journal
- Accession number :
- 39164279
- Full Text :
- https://doi.org/10.1038/s41467-024-51433-3