Fabian Theis described SEGen as a nonlinear model that applied a linear transformation in latent space and performed reasonably well.

“Like my, my lab, we started this perturbation prediction modeling and, and using machine learning in twenty nineteen with a paper we called SEGen at the time, which was like it was a nonlinear model, but essentially sort of in the latent space, did some linear transformation, and it was actually quite okay.”