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MultiSpaceNet: graph-based joint representation learning for paired spatial transcriptome–proteome data
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DOI：10.1093/bioinformatics/btag676.png)
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Abstract
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Paired spatial assays now measure the transcriptome and the proteome on the same tissue section. Existing integration methods commit to a structural choice at the outset: a separate graph per modality, early fusion that folds the measured protein into the embedding so it can no longer be predicted, or intersection rules that discard most similarity edges. Each choice limits what one trained model can do afterwards.
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