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
A Bayesian framework for efficient and accurate variant prediction.
Qian D et al.
Plos One, 2018
30212499 (PubMed )
Supplement: pone.0203553.s008.xlsx › Variants
Select a paper to view details