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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.
The development, interpretation, and validation of AI models as key considerations for their integration and analysis are discussed, and practical suggestions for improving the reliability and biological relevance of AI-driven discoveries are provided.
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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