Associated Papers
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.
Ben-Cohen G et al.
Briefings in Bioinformatics, 2022
35043155 (PubMed )
Supplement: supplementary_information_bbab524.pdf
Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.
G V et al.
Frontiers in Genetics, 2022
36561320 (PubMed )
Supplement: Table2.xlsx › TP53
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_3.xlsx › Sheet1
Identification and functional characterization of new missense SNPs in the coding region of the TP53 gene.
Doffe F et al.
Cell Death and Differentiation, 2021
33257846 (PubMed )
Supplement: 41418_2020_672_MOESM3_ESM.xlsx › Table_S2c
Lung adenocarcinoma in a patient with Li-Fraumeni syndrome bearing a novel germ-line mutation, TP53R333Vfs*12.
Takahashi S et al.
Japanese Journal of Clinical Oncology, 2020
Variable population prevalence estimates of germline TP53 variants: A gnomAD-based analysis.
de Andrade KC et al.
Human Mutation, 2019
30352134 (PubMed )
Supplement: supplement_1.xlsx › Supplementary_Table_1
Recommended Guidelines for Validation, Quality Control, and Reporting of TP53 Variants in Clinical Practice.
Leroy B et al.
Cancer Research, 2017
The relationship among p53 oligomer formation, structure and transcriptional activity using a comprehensive missense mutation library.
Kawaguchi T et al.
Oncogene, 2005
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