HIGHLIGHTS
SUMMARY
Mesenchymal stem_cells (MSCs) are the most widely studied stem_cells for cell-based therapeutic applications. Using the morphological descriptors in the stage of passage 1 (P1), the authors attempted to develop machine_learning models to quantitatively predict such potency. Two types of machine_learning models were examined-the linear regression model least absolute shrinkage and selection operator (LASSO) and the nonlinear machine_learning model random forest (RF). Morphology‑based machine_learning for predicting serial‑passage potency The authors next investigated the development of machine_learning models with morphological information to enable the quantitative prediction of "serial-passage potencies" of RECs . . .
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