Additional file 7 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 7: Fig. S7. The expression of 12 characteristic genes of the risk signature in HCC from HCCDB database. Diff: the number of differentially expressed datasets; Red/Blue for consensus up-regulated/down-regulated. Prognosis: the number of significant datasets by survival analysis; Red/Blue for Unfavorable/Favorable. HCC/All Tumor: Red/Blue for positive/negative fold change in log2 scale by comparing HCC with all tumors (TCGA data). HCC/All Adjacent: Red/Blue for positive/negative fold change in log2 scale by comparing HCC with all adjacent samples (TCGA data). HCC/Adjacent: Red/Blue for positive/negative fold change in log2 scale by comparing HCC with adjacent samples (HCCDB data). Liver Other Normal: Red/Blue for positive/negative fold change in log2 scale by comparing liver with normal tissues (GTEx&TCGA data)

Additional file 14 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 14: Table S7. The clinical traits of TCGA-LIHC cohort

Additional file 12 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 12: Table S5. Univariate and multivariate Cox regression analysis of risk score and other clinical traits for OS in TCGA-LIHC cohort

Additional file 5 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 5: Fig. S5. The Kaplan-Meier survival curve of the 12-gene signature for HCC patients with various clinicopathological characters in TCGA-LIHC cohort

Additional file 10 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 10: Table S3. Characteristic genes with significant prognostic value

Additional file 3 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 3: Fig. S3. The Kaplan-Meier analysis results of OS and RFS about 33 characteristic genes. The left of each sub-figure is about OS and the right is about RFS

Additional file 2 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 2: Fig. S2. Construction of hub gene network

Additional file 1 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 1: Fig. S1. Preprocessing of training data

Additional file 15 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 15: Doc. S1. Supplementary method for the development of the model

Additional file 9 of Machine-learning algorithms based on personalized pathways for a novel predictive model for the diagnosis of hepatocellular carcinoma

Additional file 9: Table S2. g:Profiler enrichment result