Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

A goal of cancer research is to reveal cell subsets linked to continuous clinical outcomes to generate new therapeutic and biomarker hypotheses. We introduce a machine learning algorithm, Risk Assessment Population IDentification (RAPID), that is unsupervised and automated, identifies phenotypically...

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Detalles Bibliográficos
Autores principales: Leelatian, Nalin, Sinnaeve, Justine, Mistry, Akshitkumar M, Barone, Sierra M, Brockman, Asa A, Diggins, Kirsten E, Greenplate, Allison R, Weaver, Kyle D, Thompson, Reid C, Chambless, Lola B, Mobley, Bret C, Ihrie, Rebecca A, Irish, Jonathan M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
Materias:
Computational and Systems Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340505/
https://www.ncbi.nlm.nih.gov/pubmed/32573435
http://dx.doi.org/10.7554/eLife.56879

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340505/
https://www.ncbi.nlm.nih.gov/pubmed/32573435
http://dx.doi.org/10.7554/eLife.56879

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