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