aceSerum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder
The diagnosis of major depressive disorder (MDD) currently relies on subjective clinical assessment, underscoring the need for objective biomarkers. Glycosylation, a common post-translational modification, is involved in neuroinflammation and immune regulation, both implicated in MDD pathogenesis. This study aimed to identify serum protein glycopatterns as biomarkers for MDD diagnosis and severity stratification. Serum samples from 150 individuals, including healthy volunteers (HV, n = 38), mild-to-moderate MDD…
Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder: These features were used to train seven machine-learning models, among which K-nearest neighbours (KNN) performed best, achieving 95.3% ± 2.7% accuracy and an AUC (Micro) of 0.990 ± 0.010 in a nested 5-fold cross-validation framework.
Evidence
- Peer-reviewedGlycobiology2026-09-21
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Truvace Impact Record TRV-2026-1164, v1: “Serum Protein Glycopatterns as Biomarkers for Machine Learning Diagnosis of Major Depressive Disorder.” Truvace, 2026-09-22. /record/TRV-2026-1164 (accessed at citation time). sha256 fdb47f97f44180fc…
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