We introduce a dual-view graph neural network (GNN) framework called scNET that integrates scRNA-seq data with protein–protein interaction networks. This approach enhances the characterization of gene functions, pathways and gene–gene relationships and improves cell clustering and the identification of differentially activated biological pathways across conditions.
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Fig. 1: The autoencoder model architecture.
References
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This is a summary of: Sheinin, R. et al. scNET: learning context-specific gene and cell embeddings by integrating single-cell gene expression data with protein–protein interactions. Nat. Methods. https://doi.org/10.1038/s41592-025-02627-0 (2025).
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A graph neural network that combines scRNA-seq and protein–protein interaction data. Nat Methods (2025). https://doi.org/10.1038/s41592-025-02628-z
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Published:18 March 2025
DOI:https://doi.org/10.1038/s41592-025-02628-z
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