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
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
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
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
30212499 (PubMed )
Supplement: pone.0203553.s008.xlsx › Variants
Benchmarking mutation effect prediction algorithms using functionally validated cancer-related missense mutations.
Martelotto LG et al.
Genome Biology, 2014
25348012 (PubMed )
Supplement: 13059_2014_484_MOESM2_ESM.xlsx › Additional_file_2
A one-step prescreening for point mutations and large rearrangement in BRCA1 and BRCA2 genes using quantitative polymerase chain reaction and high-resolution melting curve analysis.
Coulet F et al.
Genetic Testing and Molecular Biomarkers, 2010
BRCA1 and BRCA2 mutations in breast and ovarian cancer syndrome: reflection on the Creighton University historical series of high risk families.
Sinilnikova OM et al.
Familial Cancer, 2006
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