Research graph
References from Automated machine learning–Assisted interpretation of GC–MS urinary organic acid profiles by integrating explicit metabolic markers and chromatographic patterns for rare organic acidemias. Local targets link to admitted publications; unresolved targets remain external evidence.
“Classical organic acidurias”: diagnosis and pathogenesis
10.1007/s10238-016-0435-0 · 2017 · External reference
Topical review article: organic acidurias: a review. Part 1
10.1177/088307389100600302 · 1991 · External reference
Diagnosis and clinical features of organic acidemias: a hospital-based study in a single center in Damascus, Syria
10.5339/qmj.2015.9 · 2015 · External reference
Newborn screening
2023 · External reference
Fatty acid oxidation disorders
10.21037/atm.2018.10.57 · 2018 · External reference
The human urine metabolome
10.1371/journal.pone.0073076 · 2013 · External reference
Expanded newborn screening of inherited metabolic disorders by tandem mass spectrometry: clinical and laboratory aspects
10.1016/j.clinbiochem.2005.12.009 · 2006 · External reference
Targeted analysis of organic acids with GC–MS/MS: challenges and prospects
10.1016/j.ab.2024.115620 · 2024 · External reference
Automated, simplified GC/MS data processing system for organic acidemia screening and its application
1999 · External reference
Deconvolution gas chromatography/mass spectrometry of urinary organic acids—potential for pattern recognition and automated identification of metabolic disorders
10.1002/(sici)1097-0231(19990228)13:4<279::aid-rcm478>3.0.co;2-i · 1999 · External reference
Using random forest to detect multiple inherited metabolic diseases simultaneously based on GC–MS urinary metabolomics
10.1016/j.talanta.2021.122720 · 2021 · External reference
Training machine learning models to detect rare inborn errors of metabolism (IEMs) based on GC–MS urinary metabolomics for diseases screening
10.1016/j.ijmedinf.2024.105765 · 2025 · External reference
GC–MS urinary metabolomics analysis of inherited metabolic diseases and stable metabolic biomarker screening by a comprehensive chemometric method
10.1016/j.microc.2021.106350 · 2021 · External reference
Robust classification and biomarker discovery of inherited metabolic diseases using GC–MS urinary metabolomics analysis combined with chemometrics
10.1016/j.microc.2023.108600 · 2023 · External reference
CAP/ACMG proficiency testing for biochemical genetics laboratories: a summary of performance
10.1038/gim.2017.61 · 2018 · External reference
Urinary organic acid profiling by GC–MS reveals distinct metabolic signatures for non-invasive diagnosis and disease activity monitoring of inflammatory bowel disease
10.3390/ijms27146318 · 2026 · External reference
Unresolved reference
2018 · External reference
Laboratory analysis of organic acids, 2018 update: a technical standard of the American College of Medical Genetics and Genomics (ACMG)
10.1038/gim.2018.45 · 2018 · External reference
Artifacts in trimethylsilyl derivatization reactions and ways to avoid them
10.1016/s0021-9673(99)00267-8 · 1999 · External reference
Challenges in routine clinical chemistry testing analysis of small molecules
2019 · External reference
Analysis of metabolomic data using support vector machines
10.1021/ac800954c · 2008 · External reference
Achieving expert-level interpretation of serum protein electrophoresis through deep learning driven by human reasoning
10.1093/clinchem/hvab133 · 2021 · External reference
Economic evaluation of AI-assisted technologies in healthcare: a systematic review
10.38212/2224-6614.3570 · 2025 · External reference
Federated learning in multimodal healthcare diagnostics: privacy-preserving AI for biomedical imaging, electronic health records, wearables, and clinical decision support
10.1007/s11831-026-10629-0 · 2026 · External reference
Machine learning methods for analysis of metabolic data and metabolic pathway modeling
10.3390/metabo8010004 · 2018 · External reference
Application of a deep neural network to metabolomics studies and its performance in determining important variables
10.1021/acs.analchem.7b03795 · 2018 · External reference
Exploratory data analysis for investigating GC–MS biomarkers
2008 · External reference
Deep learning
10.1038/nature14539 · 2015 · External reference
Structural analysis and optimization of convolutional neural networks with a small sample size
10.1038/s41598-020-57866-2 · 2020 · External reference
Sample size requirements for popular classification algorithms in tabular clinical data: empirical study
10.2196/60231 · 2024 · External reference
