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 genetic variants in pheochromocytoma/paraganglioma: single-center experience.
Lima JV et al.
Endocrine Oncology (bristol, England), 2023
37529773 (PubMed )
Supplement: supplementary_table_1.pdf
Breast cancer risks associated with missense variants in breast cancer susceptibility genes.
Dorling L et al.
Genome Medicine, 2022
35585550 (PubMed )
Supplement: 13073_2022_1052_MOESM2_ESM.xlsx › Table_S14
Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.
Reza MN et al.
Scientific Reports, 2021
34584144 (PubMed )
Supplement: 41598_2021_98547_MOESM1_ESM.zip › Supplementary_files-5-8-21 › Supplementary_file_2.xlsx › Sheet1
Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies.
Gemović B et al.
Plos One, 2021
33395407 (PubMed )
Supplement: pone.0244948.s001.pdf
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_MOESM5_ESM.xlsx › SD2
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