Qingchen Zhang

ProST: an image prompt-guided multimodal representation learning framework for spatial domain identification

Abstract Motivation Spatial transcriptomics (ST) enables gene expression profiling while preserving the spatial organization of tissues, providing a powerful tool for dissecting tissue architecture and cellular heterogeneity. However, existing methods tend to prioritize improvements in clustering performance and overlo...

PRIME: A Decoupled Multi-agent Actor-Critic for Multi-view Clustering

Deep multi-view clustering draws plentiful attention in various domains, owing to remarkable performance in learning patterns from complementary information of multi-view data. However, previous methods encounter two challenges. They utilize a single pre-defined clustering strategy to perceive diverse structures from d...

DAHGT-CCI: Dynamic Heterogeneous Graph Transformer for Spatial Transcriptomics Cell-Cell Interaction Inference

It is demonstrated that DAHGT-CCI can more accurately reconstruct cell communication networks in complex tissue microenvironments, offering an indispensable computational tool for studying developmental processes, disease mechanisms, and potential therapeutic targets from a spatially resolved perspective.

3D reconstruction of spatial transcriptomics data with dual incremental deep reinforcement learning