390-CellOT: Learning Single-Cell Perturbation Responses
The paper introduces CellOT, a machine learning framework designed to predict how individual cells respond to biological stimuli, such as drugs or genetic changes. Because measuring a cell's molecular state typically destroys it, researchers cannot observe the same cell before and after a treatment; CellOT overcomes this "pairing problem" by using neural optimal transport to map unperturbed cell populations to perturbed ones. By employing input convex neural networks, the model learns the most likely transition trajectories for heterogeneous cell subpopulations based on a principle of minimal…
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