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
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
Selective identification of somatic mutations in pancreatic cancer cells through a combination of next-generation sequencing of plasma DNA using molecular barcodes and a bioinformatic variant filter.
Kukita Y et al.
Plos One, 2018
29451897 (PubMed )
Supplement: pone.0192611.s007.xlsx › tableSX
The relationship among p53 oligomer formation, structure and transcriptional activity using a comprehensive missense mutation library.
Kawaguchi T et al.
Oncogene, 2005
In vitro evolution of thermostable p53 variants.
Matsumura I et al.
Protein Science : a Publication of the Protein Society, 1999
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