Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer
Fig 3
Introducing the independent TCGA cohort improves MB patient classification.
(A) Schematic representing the training strategy: rescaled data from the entire TCGA cohort were merged with 4/5 of the MB cohort to train the T-GAN-D, which was subsequently used to predict the risk class of the remaining 1/5 of MB patients. The process was iterated 5 times. (B) Kaplan-Meier curves based on the pooled predictions of the T-GAN-D trained on both cohorts. (C) Kaplan-Meier curves separating low vs. high risk patients predicted with the CNN that was trained after merging the MB and the TCGA cohorts. The area between the curves (ABC) between Low risk (blue dashed line) and Predicted low risk (solid blue line), Predicted low risk and Predicted high risk (solid red line), Predicted high risk and high risk groups (dashed red line) are shown top to bottom in B and C.