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.
Visualizing data using t-SNE
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VERA produces ranked region-based visual explanations for 2D embeddings by associating informative areas with user features and filtering candidates automatically.
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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.
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VERA: Generating Visual Explanations of Two-Dimensional Embeddings via Region Annotation
VERA produces ranked region-based visual explanations for 2D embeddings by associating informative areas with user features and filtering candidates automatically.