Associated Papers
Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.
Ahmad RM et al.
Briefings in Bioinformatics, 2025
41263939 (PubMed )
Supplement: supplementary_materials_bbaf611_1_.zip › Supplementary_materials_bbaf611 › Supplementary_Table_S8.xlsx › Sheet1
The evolutionary impact of childhood cancer on the human gene pool.
Stoltze UK et al.
Nature Communications, 2024
38424437 (PubMed )
Supplement: 41467_2024_45975_MOESM4_ESM.xlsx › Supplementary_Data_5
Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer.
Keskin Karakoyun H et al.
Frontiers in Genetics, 2023
36896237 (PubMed )
Supplement: Table1.xlsx › clinvar_breastcancer_allvariant
A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants.
Qorri E et al.
International Journal of Molecular Sciences, 2022
35887294 (PubMed )
Supplement: ijms-23-07946-s001.zip › Supplementary_Table_S4.xlsx › BRCA2_Dataset
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
TAPES: A tool for assessment and prioritisation in exome studies.
Xavier A et al.
Plos Computational Biology, 2019
31613886 (PubMed )
Supplement: pcbi.1007453.s002.xlsx › SoftVsPanel
REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.
Tian Y et al.
Scientific Reports, 2019
31484976 (PubMed )
Supplement: 41598_2019_49224_MOESM2_ESM.xlsx › Sheet1
Toward automation of germline variant curation in clinical cancer genetics.
Ravichandran V et al.
Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2019
30787465 (PubMed )
Supplement: NIHMS1525468-supplement-Large_Excel_File.xlsx › Table_S6
Insights into BRCA Cancer Predisposition from Integrated Germline and Somatic Analyses in 7632 Cancers.
Yost S et al.
Jnci Cancer Spectrum, 2019
31360904 (PubMed )
Supplement: supplementary_table_pkz028.zip › 20181104_Supplementary_Tables.xlsx › Supplementary_Table_2
CharGer: clinical Characterization of Germline variants.
Scott AD et al.
Bioinformatics (oxford, England), 2019
30102335 (PubMed )
Supplement: bioinformatics_35_5_865_s4.xlsx › SupplTable2
Population and breast cancer patients' analysis reveals the diversity of genomic variation of the BRCA genes in the Mexican population.
Fernández-Lopez JC et al.
Human Genomics, 2019
30630528 (PubMed )
Supplement: 40246_2018_188_MOESM3_ESM.xlsx › Hoja1
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
30212499 (PubMed )
Supplement: pone.0203553.s008.xlsx › Variants
High prevalence of BRCA1 and BRCA2 mutations in unselected Nigerian breast cancer patients.
Fackenthal JD et al.
International Journal of Cancer, 2012
Evaluation of unclassified variants in the breast cancer susceptibility genes BRCA1 and BRCA2 using five methods: results from a population-based study of young breast cancer patients.
Lee E et al.
Breast Cancer Research : Bcr, 2008
18284688 (PubMed )
Supplement: bcr1865-S3.doc
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