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Learning spatial cellular motifs predictive of the responses of patients to cancer treatments

Nature Biomedical Engineering volume 6, pages 1328–1329 (2022)Cite this article

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Graph deep learning applied to multiplexed immunofluorescence data from tumour microenvironments reveals spatial cellular structures that are indicative of cancer prognosis.

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Fig. 1: SPACE-GM identifies key spatial structures from graphs.

References

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This is a summary of: Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00951-w (2022).

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Learning spatial cellular motifs predictive of the responses of patients to cancer treatments. Nat. Biomed. Eng 6, 1328–1329 (2022). https://doi.org/10.1038/s41551-022-00958-3

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