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39eaba2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # epibarrett model card
Epigenetic early detection of Barrett's esophagus / esophageal adenocarcinoma
from DNA methylation.
## Model description
This repository contains three scikit-learn pipelines trained on a biologically
calibrated HM450-style simulator:
- `lasso`: genome-wide moderated-t + L1 logistic panel
- `targeted`: VIM+CCNA1 two-gene assay analogue
- `multimodal`: methylation risk score + clinical covariates
All models are accompanied by a fitted `Preprocessor` (beta→M, probe QC,
median imputation) and the list of probe names expected at inference time.
## Intended use
Research demonstration only. Not a medical device. The intended input is a
samples × probes beta-value DataFrame (HM450 or EPIC) plus optional clinical
covariates (age, sex_male, bmi, smoker, gerd).
## How to use
```python
import joblib
import pandas as pd
bundle = joblib.load("epibarrett_model.joblib")
lasso = bundle["lasso"]
preprocessor = bundle["preprocessor"]
probe_names = bundle["probe_names"]
# X_beta is a DataFrame of beta values with the same probe columns
M = preprocessor.transform(X_beta[probe_names])
proba = lasso.predict_proba(M)[:, 1]
```
## Training data
Trained on the simulator in `epibarrett.data.simulate` (seed 7). Replace with
real GEO cohorts (GSE81334, GSE104707, etc.) for a scientific study.
## Performance (simulated demo)
| Regime | Model | AUROC | sens@spec90 | Brier |
|---|---|---|---|---|
| within | L1 panel | 0.950 | 0.873 | 0.098 |
| within | targeted VIM+CCNA1 | 0.914 | 0.754 | 0.121 |
| within | multimodal | 0.957 | 0.889 | 0.091 |
| external | L1 panel | 0.943 | 0.800 | 0.419 |
## License
MIT — see the GitHub repository for details.
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