Associated Papers
Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.
Ahmad RM et al.
Briefings in Bioinformatics, 2025
Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer.
Keskin Karakoyun H et al.
Frontiers in Genetics, 2023
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
A Comprehensive Evaluation of the Performance of Prediction Algorithms on Clinically Relevant Missense Variants.
Qorri E et al.
International Journal of Molecular Sciences, 2022
Breast cancer risks associated with missense variants in breast cancer susceptibility genes.
Dorling L et al.
Genome Medicine, 2022
Variomes: a high recall search engine to support the curation of genomic variants.
Pasche E et al.
Bioinformatics (oxford, England), 2022
Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.
G V et al.
Frontiers in Genetics, 2022
Cancer-causing BRCA2 missense mutations disrupt an intracellular protein assembly mechanism to disable genome maintenance.
Lee M et al.
Nucleic Acids Research, 2021
REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.
Tian Y et al.
Scientific Reports, 2019
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
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
Insights into BRCA Cancer Predisposition from Integrated Germline and Somatic Analyses in 7632 Cancers.
Yost S et al.
Jnci Cancer Spectrum, 2019
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
Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics.
Ernst C et al.
Bmc Medical Genomics, 2018
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
Biological and clinical evidence for somatic mutations in BRCA1 and BRCA2 as predictive markers for olaparib response in high-grade serous ovarian cancers in the maintenance setting.
Dougherty BA et al.
Oncotarget, 2017
Patterns and functional implications of rare germline variants across 12 cancer types.
Lu C et al.
Nature Communications, 2015
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations.
Martelotto LG et al.
Genome Biology, 2014
Functional assays for analysis of variants of uncertain significance in BRCA2.
Guidugli L et al.
Human Mutation, 2014
A review of a multifactorial probability-based model for classification of BRCA1 and BRCA2 variants of uncertain significance (VUS).
Lindor NM et al.
Human Mutation, 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
Ovarian cancer risk in Ashkenazi Jewish carriers of BRCA1 and BRCA2 mutations.
Satagopan JM et al.
Clinical Cancer Research : an Official Journal of the American Association for Cancer Research, 2002
Incidence of non-founder BRCA1 and BRCA2 mutations in high risk Ashkenazi breast and ovarian cancer families.
Kauff ND et al.
Journal of Medical Genetics, 2002