Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

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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Bibliographic Details
Main Authors: Nalin Leelatian, Justine Sinnaeve, Akshitkumar M Mistry, Sierra M Barone, Asa A Brockman, Kirsten E Diggins, Allison R Greenplate, Kyle D Weaver, Reid C Thompson, Lola B Chambless, Bret C Mobley, Rebecca A Ihrie, Jonathan M Irish
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2020-06-01
Series:eLife
Subjects:
machine learning
brain tumors
phoshpo-proteins
single cell
glioblastoma
mass cytomtery
Online Access:https://elifesciences.org/articles/56879

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