GNPS - Hand-on Microbiome course
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive HF
ORGANISM(S): Arabidopsis Thaliana (ncbitaxon:3702)
SUBMITTER: 
Daniel Petras
PROVIDER: MSV000094169 | GNPS | Mon Feb 26 07:46:00 GMT 2024
REPOSITORIES: GNPS
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I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
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