Extended Data Fig. 1: Data flow in the model.
From: Learning biophysical determinants of cell fate with deep neural networks
(a) Example time-lapse microscopy data showing a mixed population of MDCKWT (green) and scribkd(magenta) cells. (b) Single-cell tracking is used to build a detailed training dataset of trajectories. The single-cell track is used to extract a glimpse of the cell over time, that becomes the input data for the machine learning models. (c) The data preparation and inference pipeline. A CNN/LSTM network classifies the fate of the cell and determines the cutoff point to truncate the track to remove images that encode the fate of the cell. The goal of the machine learning model is then to learn a representation that can predict the fate of a cell (circled in white) given the local configuration during interphase. Importantly, the model does not actually observe the fate since these data fall beyond the cutoff. Images are taken at 4-minute intervals, MDCKWT cells appear in green and scribkdin magenta.
