Tumor State Representation

Decoding the fundamental states of tumors

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)

Developed machine learning approaches for single-cell representation learning and characterization of cellular heterogeneity.

SpaDo (Genome Biology, 2024)

Developed spatial transcriptomic modeling approaches to characterize spatially organized cellular states and tumor ecosystems.