We find that a simple unconditional predictor is comparable with the state-of-the-art deep learning method on genetic perturbation effect prediction.
For installation, please follow the GEARS' repo: https://github.com/snap-stanford/GEARS.
To reproduce the experiments in the project, please set random seeds from 1 to 5.
Note that similar empirical finding is also discussed in a recent paper (although I did not read it until I finished the project):
Kaspar Märtens, Rory Donovan-Maiye, and Jesper Ferkinghoff-Borg. Enhancing generative perturbation models with LLM-informed gene embeddings. ICLR 2024 MLGenX Workshop. https://openreview.net/pdf?id=eb3ndUlkt4
