CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
CMGL proposes a two-stage framework to improve cancer subtype classification by first estimating modality reliability using evidential deep learning. This frozen confidence score then guides cross-omics feature fusion and graph construction, ensuring that noisy data from unreliable omics are suppressed before making final predictions.