Furthermore, applying a machine learning (ML)-based analytical approach to a large number of stem cell-derived neural interfaces, we comprehensively mapped stem cell adhesion, differentiation, and proliferation, allowing for the cell-type-specific design of biomaterials for neural interfacing, including both adult and human-induced pluripotent stem cells (hiPSCs) with varying genetic backgrounds. In short, we were able to show how innovative combinatorial nanoarray and machine learning (ML)-driven platform technology can help with the rational design of stem cell-derived neural interfaces, which could help make tissue engineering applications more precise and individualized.
PUBLICATION: This work was recently published in Research (https://doi.org/10.34133/2022/9784273).
CORRESPONDENCE: Prof. Ki-Bum Lee (Rutgers University), https://kblee.rutgers.edu/
KBLEE Group Team: Dr. Letao Yang, Dr. Brian Conley, Dr. Christopher Rathnam, Dr. Thanapat Pongkulapa, Brandon Conklin, Yannan Hou, https://kblee.rutgers.edu/