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Fig. 1 | Biology Direct

Fig. 1

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

Fig. 1

Illustrates the identification of essential regulatory genes using the WGCNA algorithm. (A-B) Assessment of Angiogenesis Score, EMT Score, and Patient Prognosis in Glioma cases. (C-D) Specifications for constructing the WGCNA network. (E) The upper part of the figure displays the gene clustering dendrogram generated from the weighted correlation coefficients, whereas the lower part shows how genes are distributed among different modules. (F) A heatmap depicting relationships between traits and modules. (G) Map of interactions among genes related to angiogenesis and EMT. (H) Comparative analysis of the expression levels of angiogenesis and EMT-associated genes. (I) Forest plot illustrating the prognosis associated with angiogenesis and EMT-related genes. (J) Analysis of correlations among the genes linked to angiogenesis and EMT

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Biology Direct

ISSN: 1745-6150

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