metadata
license: mit
library_name: expert-model
pipeline_tag: other
tags:
- single-cell
- perturb-seq
- scRNA-seq
- perturbation-prediction
- biology
ExPert paper checkpoints
The additional checkpoints behind the ExPert paper's benchmark and ablation figures. The production model that the paper's application figures use, and that you should use for your own data, is GoyalLab/ExPert.
Code and the scripts that use these checkpoints: https://github.com/GoyalLab/ExPert (paper/)
| folder | what | used in |
|---|---|---|
fold0 … fold11 |
the 12 cross-validation models: each holds out one context (cell line / donor / condition) from training | benchmark and out-of-distribution figures |
EFFGATE3 |
the ablation arm with the gated prior pull | prior-pull ablation figures |
banks/v0.1.0_fullbank_oldfill.npz |
v0.1.0 prototypes with the earlier fill for genes never perturbed in training (trained rows identical to the model's) | ALD and PMD readouts |
Each checkpoint folder holds model.pt and gene_embeddings.npz and loads like the production model:
from huggingface_hub import snapshot_download
from expert import ExPert
path = snapshot_download("GoyalLab/ExPert-paper-models", revision="v0.1.0", allow_patterns="fold4/*")
model = ExPert.load(f"{path}/fold4")
The bank is a NumPy archive: P (perturbations × latent dims), labels, trained.
Citation
Preprint reference to be added.