Tao Huang

This study creates a single-cell spatial transcriptomic atlas of the human cortex, revealing gene expression patterns, aging effects, and disease associations across layers and species.

EAP provides a cloud-based platform for analyzing large-scale ChIP/ATAC-seq data, generating publication-ready results and enabling epigenomic analysis in cancer and therapeutic target discovery.

This paper models the aging self-organization system using supervised learning to optimize aging networks and study molecular mechanisms of aging and related diseases.

This study introduces node2loc, a deep learning method that predicts protein subcellular locations using protein-protein network representations, achieving an MCC of 0.812.