ArticleFrontiers in molecular biosciences2021
Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Plasma metabolic profiles in alcohol use disorder: diagnostic role of arginine and emotional implications of N6-acetyl-lysine and succinic acid.BMC psychiatry · 2025Article
- Article
- Molecular Toxicology and Pathophysiology of Comorbid Alcohol Use Disorder and Post-Traumatic Stress Disorder Associated with Traumatic Brain Injury.International journal of molecular sciences · 2023Review
- Association between abnormal plasma metabolism and brain atrophy in alcohol-dependent patients.Frontiers in molecular neuroscience · 2022Article
- Proteomics and weighted gene correlated network analysis reveal glutamatergic synapse signaling in diazepam treatment of alcohol withdrawal.Frontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (
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Registered trials
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