Associated Papers
BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy.
Khandakji M et al.
Physiological Genomics, 2023
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_4.xlsx › Sheet1
Analysis of Sequence and Copy Number Variants in Canadian Patient Cohort With Familial Cancer Syndromes Using a Unique Next Generation Sequencing Based Approach.
Bhai P et al.
Frontiers in Genetics, 2021
34326862 (PubMed )
Supplement: Data_Sheet_1.zip › Supplementary_files › Supplementary_Table_S4.xlsx › Variant_summary
Systematic misclassification of missense variants in BRCA1 and BRCA2 "coldspots".
Dines JN et al.
Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2020
31911673 (PubMed )
Supplement: 41436_2019_740_MOESM2_ESM.xlsx › 1._Used_in_analysis
Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort.
Li H et al.
Genetics in Medicine : Official Journal of the American College of Medical Genetics, 2020
31853058 (PubMed )
Supplement: 41436_2019_729_MOESM2_ESM.xls › Sheet1
Competitive evolution of NSCLC tumor clones and the drug resistance mechanism of first-generation EGFR-TKIs in Chinese NSCLC patients.
Deng Q et al.
Heliyon, 2018
30603682 (PubMed )
Supplement: mmc2.xlsx › Raw_reads_info
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