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Fig. 5 | Biology Direct

Fig. 5

From: Machine learning model reveals the role of angiogenesis and EMT genes in glioma patient prognosis and immunotherapy

Fig. 5

depicts the constructed model utilizing the RSF algorithm, recognized as the most efficient prognostic model. (A) Shows AUC values for diagnostic models formulated through various algorithmic combinations. (B) Displays the number of genes included by different algorithms. (C-E) Present analyses of risk scores from two different datasets. (F-H) Investigate the predictive capacity of risk scores concerning the prognosis of glioma patients. (I) Examines the differences in immune cell infiltration levels between high-risk and low-risk cohorts. (J) Forecasts patient responses to anticipated immune checkpoint inhibitors across various clusters using the TIDE algorithm. (F) Compares differences in Temozolomide IC50 values between high-risk and low-risk groups

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Biology Direct

ISSN: 1745-6150

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