Spatial technologies that jointly profile RNA and additional molecular layers provide a comprehensive view of tissue organization, yet their limited resolution results in measurements that reflect mixtures of multiple cell types. While RNA-based deconvolution methods are well established, incorporating co-profiled non-...
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.· 1 citation
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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