Yongtian Wang

VISGP identifies spatially variable genes and cellular interactions from spatial transcriptomics data, outperforming existing methods in detecting ligand-receptor pairs.

This paper introduces stMSA, a deep learning model for integrating spatial omics data, improving domain detection, matching, and alignment across tissue slices.

DisConST improves spatial domain detection in spatial transcriptomics by using distribution-aware contrastive learning, achieving better accuracy than existing methods.

This paper introduces ALG-DDI, a multi-scale feature fusion model for drug-drug interaction prediction that outperforms existing methods by integrating attribute, local biological, and global semantic information.