Associated Papers
Phenotypic evaluation of deep learning models for classifying germline variant pathogenicity.
Chow RD et al.
Npj Precision Oncology, 2024
39427061 (PubMed )
Supplement: 41698_2024_710_MOESM2_ESM.xlsx › S3
Germline Pathogenic Variants in 7636 Japanese Patients With Prostate Cancer and 12 366 Controls.
Momozawa Y et al.
Journal of the National Cancer Institute, 2020
31214711 (PubMed )
Supplement: djz124_supplementary_data.zip › djz124-Suppl_Data › Supplementary_Tables_5_and_6_060619.xlsx › Supplementary_Table_6
A pan-cancer analysis of synonymous mutations.
Sharma Y et al.
Nature Communications, 2019
31189880 (PubMed )
Supplement: 41467_2019_10489_MOESM5_ESM.xlsx › Data_S3_ClinVar___Cancer
Germline pathogenic variants of 11 breast cancer genes in 7,051 Japanese patients and 11,241 controls.
Momozawa Y et al.
Nature Communications, 2018
30287823 (PubMed )
Supplement: 41467_2018_6581_MOESM5_ESM.xlsx › SD2
Novel recurrently mutated genes and a prognostic mutation signature in colorectal cancer.
Yu J et al.
Gut, 2015
24951259 (PubMed )
Supplement: gutjnl-2013-306620-s13.pdf
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