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
Clinical, splicing, and functional analysis to classify BRCA2 exon 3 variants: Application of a points-based ACMG/AMP approach.
Thomassen M et al.
Human Mutation, 2022
35979650 (PubMed )
Supplement: HUMU-43-1921-s002.XLSX › Supp_T3
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
Breast cancer risks associated with missense variants in breast cancer susceptibility genes.
Dorling L et al.
Genome Medicine, 2022
35585550 (PubMed )
Supplement: 13073_2022_1052_MOESM2_ESM.xlsx › Table_S16
Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.
G V et al.
Frontiers in Genetics, 2022
36561320 (PubMed )
Supplement: Table2.xlsx › BRCA2
Cancer-causing BRCA2 missense mutations disrupt an intracellular protein assembly mechanism to disable genome maintenance.
Lee M et al.
Nucleic Acids Research, 2021
33978741 (PubMed )
Supplement: gkab308_supplemental_file.pdf
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
Germline variation in BRCA1/2 is highly ethnic-specific: Evidence from over 30,000 Chinese hereditary breast and ovarian cancer patients.
Bhaskaran SP et al.
International Journal of Cancer, 2019
30702160 (PubMed )
Supplement: IJC-145-962-s006.xlsx › BRCA2_ethnic_sources_
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
Germline mutations in candidate predisposition genes in individuals with cutaneous melanoma and at least two independent additional primary cancers.
Pritchard AL et al.
Plos One, 2018
29641532 (PubMed )
Supplement: pone.0194098.s003.xlsx › UK10K_AD
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
30212499 (PubMed )
Supplement: pone.0203553.s008.xlsx › Variants
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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