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Article

TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.

Briefings in bioinformatics - 10 Mar 2022

Ben-Cohen Gil, Doffe Flora, Devir Michal, Leroy Bernard, Soussi Thierry, Rosenberg Shai

Abstract excerpt

Correctly identifying the true driver mutations in a patient's tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Source

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Identifiers

PMID: 35043155
DOI: 10.1093/bib/bbab524
PMCID: PMC8921628

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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