ArticleScientific reports2020
Predicting human health from biofluid-based metabolomics using machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Multi-omics analysis of associations between host demographics and saliva metabolome, sugar profiles, and microbiome profiles.Scientific reports · 2026Article
- Harnessing Metabolomics to Advance Nutrition-Based Therapeutics for Inflammation: A Systematic Review of Randomized Clinical Trials.Metabolites · 2025Review
- Metabolomics and nutrient intake reveal metabolite-nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study.Nutrition journal · 2025Article
- Exploratory Metabolomic and Lipidomic Profiling in a Manganese-Exposed Parkinsonism-Affected Population in Northern Italy.Metabolites · 2025Article
- Integrating NMR and MS for Improved Metabolomic Analysis: From Methodologies to Applications.Molecules (Basel, Switzerland) · 2025Review
- Multi-biofluid metabolomics analysis of allergic respiratory rhinitis and asthma in early childhood.The World Allergy Organization journal · 2025Article
- Identification of clinically relevant profiles in colorectal cancer through integrated analysis of bacterial DNA and metabolome in serum.Frontiers in immunology · 2025Article
- Metabolomic and Lipidomic Analysis of Manganese-Associated Parkinsonism: a Case-Control Study in Brescia, Italy.medRxiv : the preprint server for health sciences · 2024Article
- Aqueous humor metabolomic profiling identifies a distinct signature in pseudoexfoliation syndrome.Frontiers in molecular biosciences · 2024Article
- Exploring metabolic anomalies in COVID-19 and post-COVID-19: a machine learning approach with explainable artificial intelligence.Frontiers in molecular biosciences · 2024Article
- To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.Signal transduction and targeted therapy · 2023Review
- Review
- Metabolomic profiling of CSF and blood serum elucidates general and sex-specific patterns for mild cognitive impairment and Alzheimer's disease patients.Frontiers in aging neuroscience · 2023Article
- Global Metabolomics Discovers Two Novel Biomarkers in Pyridoxine-Dependent Epilepsy Caused by ALDH7A1 Deficiency.International journal of molecular sciences · 2022Article
- Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022Review
- Article
- Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease.Scientific reports · 2022Article
- Comparative metabolomics analysis of bronchial epithelium during barrier establishment after allergen exposure.Clinical and translational allergy · 2021Article
- Metabolomics in the Diagnosis and Prognosis of COVID-19.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biofluid-based metabolomics has the potential to provide highly accurate, minimally invasive diagnostics. Metabolomics studies using mass spectrometry typically reduce the high-dimensional data to only a small number of statistically significant features, that are often chemically identified-where each feature corresponds to a mass-to-charge ratio, retention time, and intensity. This practice may remove a substantial amount of predictive signal. To test the utility of the complete feature set, we train machine learning models for health state-prediction in 35 human metabolomics studies, representing 148 individual data sets. Models trained with all features outperform those using only significant features and frequently provide high predictive performance across nine health state categories, despite disparate experimental and disease contexts. Using only non-significant features it is still often possible to train models and achieve high predictive performance, suggesting useful predictive signal. This work highlights the potential for health state diagnostics using all metabolomics features with data-driven analysis.
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Registered trials
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