# Gemeo training pipelines (Phase 2)

These scaffolds turn the bootstrap `gemeo/` runtime into a SOTA learned
digital twin. Each script is **self-contained** and produces one checkpoint
that the runtime auto-discovers.

## Prerequisites

```bash
pip install torch torch_geometric tqdm
# optional, for TxGNN starter:
pip install pyhealth
```

GPU strongly recommended (A100 or RTX 4090). Fits in 24 GB VRAM with the
default batch sizes.

## Pipeline

```
primekg.py    → data/primekg.pt        (~5 GB once)
hgt.py        → gemeo/artifacts/hgt_patient_encoder.pt
txgnn.py      → gemeo/artifacts/txgnn.pt
tgnn.py       → gemeo/artifacts/tgnn_trajectory.pt
neuralsurv.py → gemeo/artifacts/neuralsurv.pt
```

The runtime checks each artifact path on call; if missing, falls back to
the bootstrap path (no breakage).

## Datasets

| Source | Use | License |
|---|---|---|
| PrimeKG (Harvard) | KG backbone for HGT/TxGNN | MIT |
| HPO + HPO Annotation | phenotype hierarchy + disease annotations | CC-BY |
| Orphanet (XML) | rare disease ontology | CC-BY |
| ClinicalTrials.gov | trial features | public domain |
| `gemeo/feedback.jsonl` | active-learning labels from production | private |
| RareBench / RareBench-BR | held-out evaluation | varies |

## Citation

If you use any of these checkpoints, cite:

> Timmers D, Kawassaki A. *Gemeo: Heterogeneous graph foundation model for rare disease digital twins grounded in Brazilian SUS.* Raras, 2026.