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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Detalhes bibliográficos
Publicado no:eLife
Main Authors: 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: Artigo
Idioma:Inglês
Publicado em: eLife Sciences Publications, Ltd 2020
Assuntos:
Computational and Systems Biology
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7340505/
https://ncbi.nlm.nih.gov/pubmed/32573435
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.7554/eLife.56879
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