Spatial transcriptomics (ST) enables the simultaneous measurement of high-throughput gene expression and spatial structural information, offering a powerful means to decipher tissue heterogeneity. However, current spatial domain identification methods struggle to accurately distinguish continuous biological boundaries,...
Jiazhou Chen, Ziru Xiao, Junyu Li et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes a plug-and-play feature causality decomposition method for multimodal representation learning from causality perspective, which can be integrated into existing models with no affects on the original model structures.
Ye Liu, Zihan Ji, Hongmin Cai· Neural Information Processin...· 3 citations
Spatial transcriptomics (ST) enables the simultaneous measurement of high-throughput gene expression and spatial structural information, offering a powerful means to decipher tissue heterogeneity. However, current spatial domain identification methods struggle to accurately distinguish continuous biological boundaries,...
Jiazhou Chen, Ziru Xiao, Junyu Li et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes a novel deep SSL framework that enhances label propagation using tensor-based similarity, enabling the modeling of high-order relationships among multiple samples, and significantly enhances the prediction performance from limited labeled data in the HDLSS setting.
Hongmin Cai, Jiali Sun, Fei Qi et al.· IEEE Transactions on Neural...· 0 citations
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