Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic dat...

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Autores principales:	Benjamin Vittrant, Mickael Leclercq, Marie-Laure Martin-Magniette, Colin Collins, Alain Bergeron, Yves Fradet, Arnaud Droit
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Frontiers Media S.A. 2020-11-01
Colección:	Frontiers in Genetics
Materias:	machine learning prostate cancer RNA-seq biochemical recurrence random forest predictive signature
Acceso en línea:	https://www.frontiersin.org/articles/10.3389/fgene.2020.550894/full
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