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

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, w...

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Detalles Bibliográficos
Autores principales: Ben-Cohen, Gil, Doffe, Flora, Devir, Michal, Leroy, Bernard, Soussi, Thierry, Rosenberg, Shai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Problem Solving Protocol
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921628/
https://www.ncbi.nlm.nih.gov/pubmed/35043155
http://dx.doi.org/10.1093/bib/bbab524

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8921628/
https://www.ncbi.nlm.nih.gov/pubmed/35043155
http://dx.doi.org/10.1093/bib/bbab524

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