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Machine learning inference of continuous single-cell state transitions paper published!

We demonstrate that machine learning can be applied to live cell imaging data to measure gradual single cell state transitions, and apply it to quantitatively monitor myoblast differentiation during muscle fiber formation!


This was a joint project with Ori Avinoam’s lab (Weizmann Institute of Science), co-led by Amit Shakarchy (BGU, computation), Giulia Zarfati and Adi Hazak (WIS, experiments)












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