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
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.