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
Published in:	eLife Vol. 9
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
Format:	Journal Article
Language:	English
Published:	England eLife Science Publications, Ltd 23.06.2020 eLife Sciences Publications Ltd eLife Sciences Publications, Ltd
Subjects:	Algorithms Blood cancer Brain cancer Brain research brain tumors Cancer Cancer research Computational and Systems Biology Cytometry Data mining Datasets Design Glioblastoma Glioblastoma - physiopathology Glioblastomas Human Biology and Medicine Humans Immunohistochemistry Kinases Learning algorithms Machine learning mass cytomtery Medical prognosis Patients phoshpo-proteins Pilot Projects Proteins Risk assessment single cell Statistical analysis Tumor cells Tumor Cells, Cultured Tumors Unsupervised Machine Learning Variables Canada computational biology human biology systems biology single cell brain tumors glioblastoma medicine phoshpo-proteins machine learning mass cytomtery human
ISSN:	2050-084X, 2050-084X
Online Access:	Get full text
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