Using Gaussian mixture models and machine learning to predict donor- dependent megakaryocytic cell growth and differentiation potential ex vivo
January 27-31, 2019
The ability to analyze single cells via flow cytometry has resulted in a wide range of biological and medical applications. Currently, there is no established framework to compare and interpret time-series flow cytometry data for cell engineering applications. Manual analysis of temporal trends is time-consuming and subjective for large-scale datasets. We resolved this bottleneck by developing TEmporal Gaussian Mixture models (TEGM), an unbiased computational strategy to quantify and predict temporal trends of developing cell subpopulations indicative of cellular phenotype..
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William M Miller, Neda Bagheri, Darryl A. Abbott, Jia J. Wu, Nadim Mahmud, Dolores Mahmud, and Meryem K. Terzioglu, "Using Gaussian mixture models and machine learning to predict donor- dependent megakaryocytic cell growth and differentiation potential ex vivo" in "Advancing Manufacture of Cell and Gene Therapies VI", Dolores Baksh, GE Healthcare, USA Rod Rietze, Novartis, USA Ivan Wall, Aston University, United Kingdom Eds, ECI Symposium Series, (2019). http://dc.engconfintl.org/cell_gene_therapies_vi/76