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This work introduces spaGFM, a graph foundation model that serializes cellular neighborhoods through random walks to generate transformer-compatible representations of tissue organization that transfer across tissues, disease contexts, and spatial technologies.
EmitGCL is developed, a deep-learning framework to predict metastasis and related biomarkers using cancer single-cell sequencing data, enabling and validating the discovery of occult metastases and breast cancer metastasis biomarkers.
EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers, is presented and YY1 transcription factor is demonstrated as a key driver of breast cancer metastasis which was validated through in-silico and CRISPR-based migration assays, suggesting that YY1 is a potentia...
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