A multi-semantic graph learning framework with a min-cut loss improves spatial clustering, batch integration, and whole-slide scalability in spatial transcriptomics, reporting 10-20% gains over DeepST, GraphST, and IRIS.
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SemanticST: Spatially Informed Semantic Graph Learning for Clustering, Integration, and Scalable Analysis of Spatial Transcriptomics
A multi-semantic graph learning framework with a min-cut loss improves spatial clustering, batch integration, and whole-slide scalability in spatial transcriptomics, reporting 10-20% gains over DeepST, GraphST, and IRIS.