Associated Papers
BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy.
Khandakji M et al.
Physiological Genomics, 2023
Ethnic-specificity, evolution origin and deleteriousness of Asian BRCA variation revealed by over 7500 BRCA variants derived from Asian population.
Qin Z et al.
International Journal of Cancer, 2023
36385461 (PubMed )
Supplement: IJC-152-1159-s002.xlsx › BRCA1
MARGINAL: An Automatic Classification of Variants in BRCA1 and BRCA2 Genes Using a Machine Learning Model.
Karalidou V et al.
Biomolecules, 2022
36358902 (PubMed )
Supplement: biomolecules-12-01552-s001.zip › final_annotation_file_CanVaS.xlsx › Sheet1
Characterization of BRCA1-deficient premalignant tissues and cancers identifies Plekha5 as a tumor metastasis suppressor.
Liu J et al.
Nature Communications, 2020
32978388 (PubMed )
Supplement: 41467_2020_18637_MOESM5_ESM.xlsx › Bulk
Consolidated BRCA1/2 Variant Interpretation by MH BRCA Correlates with Predicted PARP Inhibitor Efficacy Association by MH Guide.
Hirotsu Y et al.
International Journal of Molecular Sciences, 2020
32486089 (PubMed )
Supplement: ijms-21-03895-s001.zip › Supplemental_Table_2.xlsx › ARUP
High prevalence of BRCA1 and BRCA2 mutations in unselected Nigerian breast cancer patients.
Fackenthal JD et al.
International Journal of Cancer, 2012
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