Figure 4
From: Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models
Interpretation of glioma deep survival models. (A) SurvivalNet learns features that are definitional (IDH mutation) or strongly associated (CDKN2A deletion, SMARCA4 mutation) with WHO genomic classification of diffuse gliomas. Feature risk scores for the top 10 of 399 features in the integrated model are shown here, in order. Each boxplot represents the risk scores for one feature across all patients. Features were ranked by median absolute risk score. (B) Kaplan-Meier plots for select features from (A). (C) A gene set enrichment analysis of transcriptional feature risk scores identified the TGF-Beta 1 signaling and epithelialmesenchymal transition (EMT) gene sets as enriched with features associated with poor prognosis. (D) Kaplan-Meier plots for select features from (C).
