zhaoyanglab/ML-for-PSC-differentiation
Code and data accompanying "A live-cell image-based machine learning strategy for reducing variability in PSC differentiation systems", Cell Discov 9, 53 (2023).
This project offers machine learning models to help stem cell researchers and biomanufacturers improve the consistency and efficiency of pluripotent stem cell (PSC) differentiation into specialized cell types like cardiomyocytes. It takes live-cell bright-field images from various stages of differentiation and outputs predictions on cell commitment, optimal chemical concentrations, and initial colony morphology, reducing variability and improving cell yield. The end-user is a cell biologist or lab technician working with PSC differentiation protocols.
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Use this if you need to standardize and optimize your PSC differentiation protocols by using AI to interpret live-cell imaging data and guide adjustments.
Not ideal if you are not working with live-cell bright-field microscopy or if your primary focus is not on reducing variability in PSC differentiation.
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Jupyter Notebook
License
MIT
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Last pushed
Feb 11, 2025
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