Associated Papers
Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.
Ahmad RM et al.
Briefings in Bioinformatics, 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
Assessment of BRCA1 and BRCA2 Germline Variant Data From Patients With Breast Cancer in a Real-World Data Registry.
Nepomuceno TC et al.
Jco Clinical Cancer Informatics, 2024
Pathogenic variants in human DNA damage repair genes mostly arose in recent human history.
Zhao B et al.
Bmc Cancer, 2024
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
Human BRCA pathogenic variants were originated during recent human history.
Li J et al.
Life Science Alliance, 2022
Variomes: a high recall search engine to support the curation of genomic variants.
Pasche E et al.
Bioinformatics (oxford, England), 2022
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
Distinct genomic subclasses of high-grade/progressive meningiomas: NF2-associated, NF2-exclusive, and NF2-agnostic.
Williams EA et al.
Acta Neuropathologica Communications, 2020
Predicting the Disease Risk of Protein Mutation Sequences With Pre-training Model.
Li K et al.
Frontiers in Genetics, 2020
REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification.
Tian Y et al.
Scientific Reports, 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
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations.
Martelotto LG et al.
Genome Biology, 2014
BRCA1 point mutations in premenopausal breast cancer patients from Central Sudan.
Biunno I et al.
Familial Cancer, 2014
Rapid evolution of BRCA1 and BRCA2 in humans and other primates.
Lou DI et al.
Bmc Evolutionary Biology, 2014
A guide for functional analysis of BRCA1 variants of uncertain significance.
Millot GA et al.
Human Mutation, 2012
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
Identification and in silico analysis of functional SNPs of the BRCA1 gene.
Rajasekaran R et al.
Genomics, 2007
Natural selection and mammalian BRCA1 sequences: elucidating functionally important sites relevant to breast cancer susceptibility in humans.
Burk-Herrick A et al.
Mammalian Genome : Official Journal of the International Mammalian Genome Society, 2006
Analysis of missense variation in human BRCA1 in the context of interspecific sequence variation.
Abkevich V et al.
Journal of Medical Genetics, 2004