| # 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. |
| |
