Author

S. M. Barone

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Cancer Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:cell-cell communication· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:clustering· 1 Approach:experimental assay· 1 Assay:flow cytometry· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

J. M. Irish· 2 shared A. Boissonnas· 1 shared A. Corneau· 1 shared A. M. Mistry· 1 shared A. Meghraoui-Kheddar· 1 shared A. R. Greenplate· 1 shared B. C. Mobley· 1 shared B. G. Chousterman· 1 shared

Activity by Year

2019-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersImmunology

J. M. Irish

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2 papers

Recent·Most cited

Two new immature and dysfunctional neutrophil cell subsets define a predictive signature of sepsis useable in clinical practice

A. Meghraoui-Kheddar, B. G. Chousterman, N. Guillou, S. M. Barone, S. Granjeaud, H. Vallet, A. Corneau, K. Guessous, A. Boissonnas, J. M. Irish, C. Combadiere

The paper identifies two new immature and dysfunctional neutrophil subsets that serve as an early blood signature for sepsis, detectable by routine flow cytometry.

bioRxiv · May 29, 2020 · PreprintImmunology

High risk glioblastoma cells revealed by machine learning and single cell signaling profiles

N. Leelatian, J. Sinnaeve, A. M. Mistry, S. M. Barone, K. E. Diggins, A. R. Greenplate, K. D. Weaver, R. C. Thompson, L. B. Chambless, B. C. Mobley, R. A. Ihrie, J. M. Irish

This paper introduces RAPID, an unsupervised machine learning algorithm that identifies single cell phenotypes and assesses clinical risk stratification in glioblastoma patients using single cell mass cytometry data.

bioRxiv · Jun 1, 2019Cancer Biology