Yang Cui

LingoCell interprets gene perturbations using natural language and tissue niches, revealing synergistic disease responses and shared fibroblast programs in cancers.

DiffusionST improves spatial transcriptomics data quality and identifies spatial domains through a diffusion model and graph convolutional network.

STAIG integrates spatial transcriptomics data with histological images using graph-contrastive learning to analyze spatial domains without prealignment.

The paper compares single-cell transcriptomes across four vertebrate species to identify conserved regulatory patterns and differentiation drivers in retinal cells.

HyGAnno transfers cell type labels from scRNA-seq to scATAC-seq using a graph neural network, improving annotation accuracy and robustness.

DenoiseST is a deep learning method for spatial transcriptomics that improves data imputation, clustering, and identification of functionally variable genes by reducing dropout noise.