Zhuo Liang

BLOOM frames transcriptomic virtual-embryo modeling as conditional generation: given an observed window of cell populations, a requested developmental stage and cell-state information, it generates the expression distribution at that stage. Pseudo-trajectories link independently sampled populations, while developmental age, cell-identity hierarchy, anatomical context, gene programmes and transcription-factor–target networks condition both a Gaussian base and a flow-matching generator. Across prenatal, visual-cortex and multi-organ endothelial mouse single-cell atlases, BLOOM ranks first for stage-averaged gene and cell correlations against 22 time-series forecasting baselines.

BLOOM frames transcriptomic virtual-embryo modeling as conditional generation: given an observed window of cell populations, a requested developmental stage and cell-state information, it generates the expression distribution at that stage. Pseudo-trajectories link independently sampled populations, while developmental age, cell-identity hierarchy, anatomical context, gene programmes and transcription-factor–target networks condition both a Gaussian base and a flow-matching generator. Across prenatal, visual-cortex and multi-organ endothelial mouse single-cell atlases, BLOOM ranks first for stage-averaged gene and cell correlations against 22 time-series forecasting baselines.