MAGNET fuses partial multimodal embeddings via dynamic attention and constructs missingness-aware patient graphs for GNN-based cancer classification, outperforming prior fusion methods on three multiomics datasets.
TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach
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Missing-Modality-Aware Graph Neural Network for Cancer Classification
MAGNET fuses partial multimodal embeddings via dynamic attention and constructs missingness-aware patient graphs for GNN-based cancer classification, outperforming prior fusion methods on three multiomics datasets.