Jie Sheng

This paper uses surprisal information-theoretic analysis on spatial transcriptomic data from human glioblastoma to uncover hidden spatial programs and interactions not detected by standard methods.

CellTRIP uses multi-agent reinforcement learning to infer dynamic virtual cell environments from single-cell gene expression data, enabling simulation and prediction of cell interactions and changes over time and space.