This repository contains code for the study:
Mohamed Adil, Teodora R. Kolarova, Anna-Lisa Doebley, Leah A. Chen, Cara Tobey, Patricia Galipeau, Sam Rosen, Michael Yang, Brice Colbert, Robert D. Patton, Thomas W. Persse, Erin Kawelo, Jonathan B. Reichel, Colin C. Pritchard, Shreeram Akilesh, Christina M. Lockwood, Gavin Ha†, Raj Shree†.
Preeclampsia risk prediction from non-invasive prenatal cell-free DNA screening.
Nature Medicine. 2025 Feb 12. doi: 10.1038/s41591-025-03509-w Online ahead of print.
Python/3.9.6-GCCcore-11.2.0
| Package | Version |
|---|---|
| Jupyterlab | 3.1.6 |
| Joblib | 1.0.1 |
| Matplotlib | 3.7.1 |
| Pandas | 2.0.2 |
| scikit-learn | 1.1.1 |
| scipy | 1.12.0 |
| seaborn | 0.11.2 |
| sklearn | 0.0.post1 |
| xgboost | 1.6.1 |
Meta data - Supplementary_Tables.xlsx
Features data - Raw_feature_tables.xlsx
Load jupyter notebook in jupyterlab
Update path to meta and feature data.
Update path for output files.
Run > Run All Cells
Trained Griffin-FF model
Trained Griffin-PE models
Figure 2G & 2H