Tumor State Translation

Connecting biological states across scales and modalities

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 Work

SpaPheno (Genome Medicine, 2026)

Developed an interpretable AI framework that connects spatial tumor states with clinical phenotypes, enabling the translation of molecular spatial patterns into patient-level outcomes.