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_table_s1_bbab524.xlsx › S1B._Negative_Set
Therapeutic implications of activating noncanonical PIK3CA mutations in head and neck squamous cell carcinoma.
Jin N et al.
The Journal of Clinical Investigation, 2021
Analysis and Interpretation of the Impact of Missense Variants in Cancer.
Petrosino M et al.
International Journal of Molecular Sciences, 2021
34063805 (PubMed )
Supplement: ijms-22-05416-s001.zip › new_supplementary_file1.xlsx › new_supplementary_file1
Reproducible Analysis of Post-Translational Modifications in Proteomes--Application to Human Mutations.
Holehouse AS et al.
Plos One, 2015
26659599 (PubMed )
Supplement: pone.0144692.s002.xlsx › suppTable_2.txt
TP53 mutations in human cancer: database reassessment and prospects for the next decade.
Leroy B et al.
Human Mutation, 2014
24665023 (PubMed )
Supplement: supplement_1.pdf
Cancer-associated p53 tetramerization domain mutants: quantitative analysis reveals a low threshold for tumor suppressor inactivation.
Kamada R et al.
The Journal of Biological Chemistry, 2011
Evaluation of transcriptional activity of p53 in individual living mammalian cells.
Imagawa T et al.
Analytical Biochemistry, 2009
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