Rory J. Maizels

This paper introduces a framework to model cell fate decisions using single-cell data, revealing conserved decision-making architectures across species.

The paper introduces a deep learning framework called velvet to model gene expression dynamics from single-cell data, outperforming existing methods and capturing developmental decision-making processes.

Human embryonic spinal cord scRNA-seq (71,219 cells, CS12-CS19) mirrors mouse patterning and neurogenesis but shows floor-plate PAX7, broad ventral NKX6.2 and early OLIG2/NKX2.2 progenitors.