Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment
Fig 5
Machine learning classifier for HCC subtypes.
(A) Workflow of the subtype SVM classifier. The process of selecting subtype-specific genes followed the differential analysis procedure described in the Methods, with genes retained based on criteria of p.adjust < 0.001 and |log2FC| > 1. The mean interval was defined as [μ−σ, μ+σ], where μ represents the average expression of the gene in the patient cohort, and σ represents the standard deviation of gene expression in the patient cohort. (B) Predictive results of the SVM_10 model on the validation set. (C) ROC curve of the SVM_10 model on the training set. (D) ROC curve of the SVM_10 model on the validation set. (E) Expression distribution of the 10 classification genes across these two subtypes. (F) Heatmap of the expression levels of the 10 classification genes in different subtypes and normal samples.