Tumor State Perturbation

Predicting and designing tumor state transitions

Understanding cancer requires not only describing tumor states, but also predicting how these states change in response to genetic and therapeutic interventions. We develop AI models to characterize perturbation-induced state transitions and identify principles governing tumor response.

Representative Works

MUSIC (Nature Communications, 2019)

Developed computational approaches for modeling genetic perturbation effects and linking perturbations to cellular phenotypic changes.

PerturBase (Nucleic Acids Research, 2025)

Developed a systematic framework and database for integrating and interpreting perturbation data to understand gene function and cellular state regulation.