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Abstract:
The ability to understand and predict molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level.
Reference:
Learning Single-Cell Perturbation Responses using Neural Optimal Transport C. Bunne, S. G. Stark, G. Gut, K. V. Lehmann, L. Pelkmans, A. Krause, G. RatschIn NeurIPS Optimal Transport and Machine Learning Workshop, 2021
Bibtex Entry:
@inproceedings{bunne2021learning,
	author = {Charlotte Bunne and Stefan G. Stark and Gabriele Gut and Kjong-Van Lehmann and Lucas Pelkmans and Andreas Krause and Gunnar Ratsch},
	booktitle = {NeurIPS Optimal Transport and Machine Learning Workshop},
	title = {Learning Single-Cell Perturbation Responses using Neural Optimal Transport},
	year = {2021}}