Associated Papers
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
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
Identification of relevant genetic alterations in cancer using topological data analysis.
Rabadán R et al.
Nature Communications, 2020
32732999 (PubMed )
Supplement: 41467_2020_17659_MOESM3_ESM.xls › TableS1
Digital next-generation sequencing identifies low-abundance mutations in pancreatic juice samples collected from the duodenum of patients with pancreatic cancer and intraductal papillary mucinous neoplasms.
Yu J et al.
Gut, 2017
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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