Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells

Nalin Leelatian(Yale University), Jonathan M. Irish(Vanderbilt University), Sierra Barone(Vanderbilt University), Justine Sinnaeve(Vanderbilt University), Kyle D. Weaver(Vanderbilt University Medical Center), Asa A. Brockman(Vanderbilt University), Akshitkumar M. Mistry(University of Louisville), Allison R. Greenplate(California University of Pennsylvania), Reid C. Thompson(Neurological Surgery), Lola B. Chambless(Vanderbilt University), Rebecca A. Ihrie(Vanderbilt University), Bret C. Mobley(Vanderbilt University Medical Center), Kirsten E. Diggins(Vanderbilt University)
eLife
June 22, 2020
10.7554/elife.56879
Cited by 33

Related Papers

Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications
|Science|2020|1.7k
Intertumoral Heterogeneity within Medulloblastoma Subgroups
|Cancer Cell|2017|1.3k
New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs
|Cell|2016|1k
mRNA vaccines induce durable immune memory to SARS-CoV-2 and variants of concern
|Science|2021|932
Comprehensive mapping of immune perturbations associated with severe COVID-19
|Science Immunology|2020|806