Multi-omics and machine learning approaches uncover metabolic biomarkers for diagnosis of germinoma

Using metabolomics and a multi-step machine learning approach, potential diagnostic metabolic biomarkers in the cerebrospinal fluid of 65 patients (26 germinoma, 31 non-iGCT, 8 NGGCT) were identified. A subset of these samples (13 plus 8/9 random samples) was used as test data to evaluate classification models.