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README.md

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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)
+- `model_card.json` — machine-readable training/eval metadata (includes internal training lineage for reproducibility)
+
+## 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 (1,396 positives / 678 negatives
+/ 184 structural negatives) plus 83 de-leaked 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          | **0.875** |
+| Structural-specificity AUROC             | **0.897** |
+| Non-yeast generalization AUROC           | **0.803** |
+| Matched-pair accuracy (held-out)         | **1.00**  |
+| Hard-18 held-out AUROC                   | **0.812** |
+| Mean margin (pos − neg) on matched pairs | **+0.130** |
+
+## 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 (Pandi et al.).
+
+## License
+
+MIT for the code; weights released for academic use under the same license.