Foundation models offer a promising paradigm for modeling spatial transcriptomics, but capturing tissue context over cellular graphs makes training at scale challenging. We introduce spaGFM, a graph foundation model that serializes cellular neighborhoods through random walks to generate transformer-compatible represent...
Yu Zhong, Fei He, Xiao-Jie Jin et al.· Research Square· 0 citations
Pathology foundation models trained from whole-slide images alone treat tissue morphology as an autonomous visual phenotype, leaving learned representations only weakly anchored to the molecular processes that generate tissue architecture. Here we present Fuji, a multimodal pathology foundation model that grounds morph...
Q. Li, J. Sang, Yiwei Xiao et al.· Research Square· 0 citations
Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding...
Xiaoying Wang, Maoteng Duan, Anthony J. Snyder et al.· Nature Communications· 0 citations
Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding...
Xiaoying Wang, Maoteng Duan, Po-Lan Su et al.· bioRxiv· 0 citations
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