Figure 5.

Learning with data from multiple cancer types improves deep survival models. (A) Data from the BRCA dataset was partitioned into training, validation, and testing sets. The BRCA training set was augmented with samples from the OV and UCEC and used to construct models for BRCA survival prediction. (B) Augmented training sets significantly improve the performance of SurvivalNet models for the integrated feature set. For the transcriptional feature set, marginal improvement was observed when training with BRCA + OV + UCEC data, but training with BRCA + OV data provides no improvement. (C) For Cox elastic net, augmentation significantly degrades performance for the high-dimensional transcriptional feature set. (D) Gene set enrichment analysis of feature risk scores from the BRCA and BRCA + OV + UCEC transcriptional models. The model trained with BRCA + OV + UCEC samples emphasizes different biological concepts than the BRCA-only model.