A novel stratification framework for predicting outcome in patients with prostate cancer

BACKGROUND: Unsupervised learning methods, such as Hierarchical Cluster Analysis, are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well-documented heterogeneous composition of prostate cancer samples. Our aim is to use more sophisticated analytic...

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Detalles Bibliográficos
Autores principales: Luca, Bogdan-Alexandru, Moulton, Vincent, Ellis, Christopher, Edwards, Dylan R., Campbell, Colin, Cooper, Rosalin A., Clark, Jeremy, Brewer, Daniel S., Cooper, Colin S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217762/
https://www.ncbi.nlm.nih.gov/pubmed/32203215
http://dx.doi.org/10.1038/s41416-020-0799-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7217762/
https://www.ncbi.nlm.nih.gov/pubmed/32203215
http://dx.doi.org/10.1038/s41416-020-0799-5

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