SMODER is presented, an RNA-anchored framework for spatial multi-omics deconvolution that treats transcriptomes as the primary determinant of cell identity while incorporating complementary molecular modalities in a constrained manner and consistently outperforms existing methods in both accuracy and spatial coherence.
Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters. This problem has been widely explored using graph neural networks (GNNs) due to their ability to leverage node attributes and graph topology for effective cluster assignments. Howev...
Wei Ju, Si-Yu Yi, Kang-Jie Zheng et al.· 3 citations
Single-cell RNA sequencing profiles cellular heterogeneity at atlas scale, making automated annotation essential. However, target datasets often contain novel cell types missing from incomplete references. We present scOLAR, an ontology-guided open-set framework that learns prototypes over the Cell Ontology and uses bo...
Results show that current pathology VLMs still exhibit a substantial gap between answer-side performance and visual-semantic binding, and this work presents PathBind, a benchmark comprising 2,600 samples that undergoes task-specific automated filtering and expert review to reduce textual shortcuts and improve entity-re...
Cheng-Yang Zhang, Wen-Chuan Zhang, Bo Li et al.· arXiv.org· 3 citations
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