Cancer molecular subtyping using limited multi-omics data with missingness
Fig 2
Diagnostic performance on the GC dataset in the standard supervised learning setting.
(a) The cancer subtype diagnosis performance of CancerSD vs. comparison methods, including kNN, RFC, AE-XGBoost, MOMA, MOGONET, DCP, and APADC on the STAD dataset.(b) Sample clustering using original data and embedded representations given by CancerSD and other methods.(c) The diagnosis performance of the tested methods under different degrees of omics missingness.(d) The diagnostic accuracy of CancerSD for different GC subtypes.(e) The diagnostic probability of CancerSD for different GC subtypes.