3 citations · 3 across the 5 of their papers we have counts for
5 papers
GROVER: Graph-guided Representation of Omics and Vision with Expert Regulation for Adaptive Spatial Multi-omics Fusion
Yongjun Xiao, Dian Meng, Xinlei Huang +4
Effectively modeling multimodal spatial omics data is critical for understanding tissue complexity and underlying biological mechanisms. While spatial transcriptomics, proteomics,…
Out-of-Distribution Graph Models Merging
Yidi Wang, Ziyue Qiao, Jiawei Gu +4
This paper studies a novel problem of out-of-distribution graph models merging, which aims to construct a generalized model from multiple graph models pre-trained on different doma…
Single-View Graph Contrastive Learning with Soft Neighborhood Awareness
Qingqiang Sun, Chaoqi Chen, Ziyue Qiao +2
Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augment…
Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios
Xinlei Huang, Jialiang Tang, Xubin Zheng +3
Knowledge Distillation (KD) transfers knowledge from a large pre-trained teacher network to a compact and efficient student network, making it suitable for deployment on resource-l…
PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis
Xinlei Huang, Zhiqi Ma, Dian Meng +5
Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes…