Tongxuan Lv

The paper presents a unified spatial transcriptomic dataset of 10 mouse organs, providing high-resolution expression matrices paired with histological images for method development and benchmarking.

This study uses deep learning to predict histone modifications across plant species, showing improved cross-species performance with family-level models.

This paper introduces SSGATE, a dual-path graph attention auto-encoder for integrating single-cell and spatial multi-omics data, showing improved performance and robustness over existing methods.