Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data
Fig 2
A. Boxplot of the C-IPCW of the 10 TCGA datasets using four prognosis-predicting methods: Cox-nnet (dropout), CoxBoost, Cox-PH (ridge) and RF-S. The data were randomly split into 80% training and 20% testing sets, and repeated 10 times. Average C-IPCWs are presented as the metric. For “overall” condition, all 10 TCGA cancer datasets are combined as one “cancer” dataset. Sign * indicates statistical significance (p < 0.05). B. Heatmap of the performance rank of each dataset, based on the order of the average C-IPCW scores. Ranks 1, 2, 3, and 4 indicate the descending performance of each computational method.