Extended Data Fig. 1: Distribution of predictability scores for feature classes in all cancer types. | Nature Cancer

Extended Data Fig. 1: Distribution of predictability scores for feature classes in all cancer types.

From: Pan-cancer image-based detection of clinically actionable genetic alterations

Extended Data Fig. 1

ao, Target features were assigned to one of four categories as shown in Supplementary Table 1: Genetic variants, oncogenic drivers, high-level signatures and standard-of-care features. For each of these classes, predictability by deep learning was assessed and the distribution of false-detection-rate (FDR)-corrected p-values is shown, with low p-values capped at 10-5. High-level signatures were highly predictable in most tumor types. p, Color legend for all panels.

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