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ZENODO
Dataset . 2023
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ZENODO
Dataset . 2023
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Data sources: ZENODO
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BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material

Authors: Khandakji, Mohannad; Habish, Hind Hassan Ahmed; Abdulla, Nawal Bakheet Salem; Kusasi, Sitti Apsa Albani; Abdou, Nema Mahmoud Ghobashy; Al-Mulla, Hajer Mahmoud; Al Sulaiman, Reem Jawad; +2 Authors

BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material

Abstract

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.

Keywords

BRCA1, BRCA2, breast cancer, ovarian cancer, variant pathogenicity, in silico predictions, variant prioritization, VUS

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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