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license: mit
tags:
- rna
- llps
- phase-separation
- rna-fm
- biology
library_name: pytorch
---
# RNAPhaseek
Predicts the probability that an RNA sequence undergoes **protein-free
liquid–liquid phase separation (LLPS)**, and powers a de-novo generator
for new LLPS-prone RNAs.
## Try it (no install)
Open the Colab notebook from the project's GitHub repo for one-click
scoring and de-novo design.
## What's in this repo
- `final_model.pt` — RNA-FM + FEGSTrans adapter + 38-dim biophysics + MLP head, 426 MB
- `norm_stats.npz` — biophysics feature mean/std (must accompany the checkpoint)
## Architecture
Three streams fused in a single MLP head:
1. **RNA-FM backbone** (`multimolecule/rnafm`, 640-dim, last 2 layers fine-tuned)
2. **FEGSTrans adapter** that pools backbone embeddings with a structural FEGS bias
3. **38 biophysical features** (MFE, GC%, G4-potential, self-complementarity, etc.)
Trained on a strict protein-free RNA-LLPS corpus (positives + negatives +
structural hard negatives) plus matched training pairs that teach the model
the free-vs-sequestered G-tract distinction — closing the structure-specificity
blind spot of earlier training recipes.
## Headline numbers
| Metric | Value |
|---|---|
| 5-fold cluster-grouped CV AUROC | **0.88** |
| Structural-specificity AUROC | **0.90** |
| Non-yeast generalization AUROC | **0.80** |
| Matched-pair accuracy (held-out) | **1.00** |
## Programmatic use
```python
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="quercuscode/rnaphaseek", filename="final_model.pt")
norm_path = hf_hub_download(repo_id="quercuscode/rnaphaseek", filename="norm_stats.npz")
# then load with the project code (see the GitHub repo):
from rnaphaseek import RNAPhaseekScorer, read_fasta
scorer = RNAPhaseekScorer(model_path=model_path, norm_path=norm_path)
probs = scorer.score(["GGGAGGGAGGGAGGGUUUUUUUUUUUUUUU"])
print(probs)
```
## Citation
If you use RNAPhaseek, please cite the accompanying manuscript (Cheraghali et al.).
## License
MIT for the code; weights released for academic use under the same license.
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