Published April 18, 2023
| Version 1.0.0.
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BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material
Authors/Creators
- 1. Hamad Bin Khalifa University
- 2. National Center for Cancer Care and Research
- 3. Hamad Bin Khalifa UniversityFigure S1: Distribution of the reviewed 141 BRCA1 missense variants; Figure S2: The Shapely values for the BRCA1 XGBoost models; Figure S3: The Shapely values of the BRCA1 XGBoost model used to predict the functional assays' results for variants of uncertain significance; Table S1: The receiver operating characteristic (ROC) curve analysis for the different in silico predic-tions; Table S2: Cross validation of the BRCA1 model in 5 different random training and test samples; Table S3: Pathogenici-ty prediction and prioritization of the 31,058 unreviewed BRCA1 variants from the BRCA Exchange database.
Description
Figure S1: Distribution of the reviewed 141 BRCA1 missense variants; Figure S2: The Shapely values for the BRCA1 XGBoost models; Figure S3: The Shapely values of the BRCA1 XGBoost model used to predict the functional assays’ results for variants of uncertain significance; Table S1: The receiver operating characteristic (ROC) curve analysis for the different in silico predictions; Table S2: Cross validation of the BRCA1 model in 5 different random training and test samples; Table S3: Pathogenicity prediction and prioritization of the 31,058 unreviewed BRCA1 variants from the BRCA Exchange database.
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