Fig. 3 | Scientific Reports

Fig. 3

From: Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma

Fig. 3

Survival analysis and predictive performance evaluation of machine learning-derived prognostic signature (MLDPS). (A-C) Kaplan-Meier survival analysis for overall survival between high and low MLDPS groups in CGGA-693 (A), CGGA-325 (B) and TCGA cohorts (C). (D-F) Univariate and multivariate Cox regression analyses regarding of MLDPS in CGGA-693 (D), CGGA-325 (E) and TCGA cohorts (F). (G-I) Time-dependent receiver-operator characteristic (ROC) analysis for predicting OS at 1-, 3- and 5-year in CGGA-693 (G), CGGA-325 (H) and TCGA cohorts (I).

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