Associated Papers
TP53 variant clusters stratify phenotypic diversity in germline carriers and reveal an osteosarcoma-prone subgroup.
Fischer NW et al.
Nature Communications, 2025
41022739 (PubMed )
Supplement: 41467_2025_63528_MOESM7_ESM.xlsx › Sheet1
Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations.
Funk JS et al.
Nature Genetics, 2025
39774325 (PubMed )
Supplement: 41588_2024_2039_MOESM5_ESM.xlsx › SuppFig6
Comprehensive classification of TP53 somatic missense variants based on their impact on p53 structural stability.
Tam B et al.
Briefings in Bioinformatics, 2024
39140857 (PubMed )
Supplement: table_s1_bbae400.xlsx › Sheet1
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_table_s1_bbab524.xlsx › S1B._Negative_Set
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Brown AL et al.
Plos Computational Biology, 2019
31034466 (PubMed )
Supplement: pcbi.1006981.s010.xlsx › final_benchmark
Complex analysis of the p53 tumor suppressor in lung carcinoma.
Smardova J et al.
Oncology Reports, 2016
Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.
Engin HB et al.
Plos One, 2016
27043210 (PubMed )
Supplement: pone.0152929.s003.xlsx › mut_effects_per_ppi
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