Yuqiao Gong

This paper introduces NTF, a deep learning framework for high-resolution 3D reconstruction of spatial omics data, achieving fast and scalable results across multiple species and datasets.

The paper introduces Meta-Encoder, a framework that combines multiple pathological foundation models to improve downstream task performance in computational pathology.

HERGAST identifies spatial structures and amplifies gene expression signals in ultra-large spatial transcriptomics data, outperforming existing methods and revealing novel biological insights in cancer.

RGAST improves cell-cell communication inference from spatial transcriptomics by integrating spatial and transcriptional data, revealing directional communication and enhancing spatial domain identification.