kmlyyll/epibarrett-model

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

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.