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
The Potential of Genetics in Identifying Women at Lower Risk of Breast Cancer.
Bolze A et al.
Jama Oncology, 2024
38153744 (PubMed )
Supplement: jamaoncol-e235468-s002.xlsx › eTable_4
Association between germline pathogenic variants and breast cancer risk in Japanese women: The HERPACC study.
Kasugai Y et al.
Cancer Science, 2022
35218119 (PubMed )
Supplement: CAS-113-1451-s002.xlsx › Table_S1
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
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_MOESM4_ESM.xlsx › SD1
Absence of somatic ATM missense mutations in 58 mammary carcinomas.
Feng J et al.
Cancer Genetics and Cytogenetics, 2003
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