Research

Tumors are not defined solely by genetic alterations or molecular subtypes, but by complex and dynamic cellular states emerging across multiple biological scales. We develop interpretable AI models to construct unified representations of tumor states from molecular, cellular, spatial, and functional perspectives. Representative Works: scLearn (Science Advances, 2020); SpaDo (Genome Biology, 2024).

A major challenge in cancer research is how to translate biological information between experimental systems, molecular layers, spatial contexts, and clinical outcomes. We develop AI-driven frameworks to establish cross-scale and cross-modal mappings that bridge basic discoveries and clinical applications. Representative Works: SpaPheno (Genome Medicine, 2026).

We aim to explore the immune and microbiome microenvironments of tumors, investigating their composition and function, as well as their impact on tumor development. By integrating multi-omics data, we seek to reveal how the tumor microenvironment influences cancer progression. Representative Works: MUSIC (Nature Communications, 2019); PerturBase (Nucleic Acids Research, 2025).