Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty

摘要

BackgroundSingle-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty.