Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy
Sierra Barone(Vanderbilt University), Jonathan M. Irish(Vanderbilt University), Joanne Lannigan(National Institutes of Health), A Paul(Manchester University NHS Foundation Trust), Judith A. Woodfolk(University of Virginia), Lyndsey M. Muehling(University of Virginia), William W. Kwok(Virginia Mason Medical Center), Ronald B. Turner(University of Virginia)
Cited by 9
Related Papers
Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy
|Nature Communications|2016|572
MIFlowCyt‐EV: a framework for standardized reporting of extracellular vesicle flow cytometry experiments
|Journal of Extracellular Vesicles|2020|514
Optimisation of imaging flow cytometry for the analysis of single extracellular vesicles by using fluorescence‐tagged vesicles as biological reference material
|Journal of Extracellular Vesicles|2019|365
<scp>OMIP‐069</scp>: Forty‐Color Full Spectrum Flow Cytometry Panel for Deep Immunophenotyping of Major Cell Subsets in Human Peripheral Blood
|Cytometry Part A|2020|359
A compendium of single extracellular vesicle flow cytometry
|Journal of Extracellular Vesicles|2023|248