Associated Papers
Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.
Ahmad RM et al.
Briefings in Bioinformatics, 2025
Classification of Gene Variants in a Danish Population with Suspected Predisposition to Hereditary Breast and/or Ovarian Cancer.
Munch AK et al.
Cancers, 2025
ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence.
Ramadane-Morchadi L et al.
American Journal of Human Genetics, 2025
Multi-locus inherited neoplasia alleles syndromes in cancer: implications for clinical practice.
Yuen J et al.
European Journal of Human Genetics : Ejhg, 2025
Pembrolizumab efficacy in a tumor mutation burden-high glioblastoma patient: A case study and implications for precision oncology.
Nishiyama A et al.
Cancer Science, 2025
VarMeter2: An enhanced structure-based method for predicting pathogenic missense variants through Mahalanobis distance.
Ohno S et al.
Computational and Structural Biotechnology Journal, 2025
Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification.
Padigepati SR et al.
Human Genetics, 2024
All exons are not created equal-exon vulnerability determines the effect of exonic mutations on splicing.
Holm LL et al.
Nucleic Acids Research, 2024
Differential Sensitivity of Germline and Somatic BRCA Variants to PARP Inhibitor in High-Grade Serous Ovarian Cancer.
Vendrell JA et al.
International Journal of Molecular Sciences, 2023
Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures.
Li C et al.
Theranostics, 2023
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
Benchmarking of SpCas9 variants enables deeper base editor screens of BRCA1 and BCL2.
Sangree AK et al.
Nature Communications, 2022
Quantifying prediction of pathogenicity for within-codon concordance (PM5) using 7541 functional classifications of BRCA1 and MSH2 missense variants.
Loong L et al.
Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2022
BRCA1 and Breast Cancer: Molecular Mechanisms and Therapeutic Strategies.
Fu X et al.
Frontiers in Cell and Developmental Biology, 2022
Population-based estimates of breast cancer risk for carriers of pathogenic variants identified by gene-panel testing.
Southey MC et al.
Npj Breast Cancer, 2021
Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.
Reza MN et al.
Scientific Reports, 2021
Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants.
Özkan S et al.
International Journal of Molecular Sciences, 2021
Analysis of Sequence and Copy Number Variants in Canadian Patient Cohort With Familial Cancer Syndromes Using a Unique Next Generation Sequencing Based Approach.
Bhai P et al.
Frontiers in Genetics, 2021
Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification.
Parsons MT et al.
Human Mutation, 2019
Predicting pathogenicity of missense variants with weakly supervised regression.
Cao Y et al.
Human Mutation, 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
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Brown AL et al.
Plos Computational Biology, 2019
Accurate classification of BRCA1 variants with saturation genome editing.
Findlay GM et al.
Nature, 2018
GEMO, a National Resource to Study Genetic Modifiers of Breast and Ovarian Cancer Risk in BRCA1 and BRCA2 Pathogenic Variant Carriers.
Lesueur F et al.
Frontiers in Oncology, 2018
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations.
Martelotto LG et al.
Genome Biology, 2014
CpG/CpNpG motifs in the coding region are preferred sites for mutagenesis in the breast cancer susceptibility genes.
Cheung LW et al.
Febs Letters, 2007
Significant contribution of large BRCA1 gene rearrangements in 120 French breast and ovarian cancer families.
Gad S et al.
Oncogene, 2002
p53 mutations in BRCA1-associated familial breast cancer.
Schlichtholz B et al.
Lancet (london, England), 1998