Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer
Fig 5
The T-GAN-D stratifies TCGA patients despite these being scarcely represented in the merged training set.
(A) Schematic representing the training strategy: rescaled data from the entire MB cohort were merged with 4/5 of the TCGA cohort to train the T-GAN-D, which was subsequently used to predict the risk class of the remaining 1/5 of TCGA patients. The process was iterated 5 times. (B) Stratification of the TCGA patients by T-GAN-D trained on the merged dataset and (C) the MB dataset alone. Kaplan-Meier curves were generated pooling the predictions of all iterations of the 5-fold CV. 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.