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• MUST explicitly decomposes each modality's representation into modality-specific and cross-modal contextualized components through algebraic constraints in a learned low-rank shared subspace.method 95%
• MUST achieves state-of-the-art performance with complete data on five TCGA cancer datasets.result 90%
• MUST maintains robust predictions in both missing pathology and missing genomics conditions.result 90%
• MUST employs conditional latent diffusion models to generate high-quality representations for missing modality-specific information.method 95%
• MUST achieves a C-index of 0.742 with complete data on TCGA datasets.result 90%
• MUST achieves a C-index of 0.716 under missing genomics conditions.result 90%
• MUST achieves a C-index of 0.739 under missing pathology conditions.result 90%
• The proposed framework has clinically acceptable inference latency.technical 85%
• MUST explicitly decomposes each modality's representation into modality-specific and cross-modal contextualized components through algebraic constraints in a learned low-rank shared subspace.method 90%
• MUST achieves state-of-the-art performance with complete data on five TCGA cancer datasets.result 90%
• MUST maintains robust predictions in missing pathology conditions, achieving a C-index of 0.739.result 90%
• MUST maintains robust predictions in missing genomics conditions, achieving a C-index of 0.716.result 90%

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