Machine Learning Approach to the Migration Patterns of Dendritic Cells
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초록
내용
We investigate migration patterns of the dendritic cells via live-cell imaging for 24-h based on a machine learning approach. Two methods, K-means clustering, and XGBoost are used for the machine learning approach. We found that there exist three modes in the context of motility and directional persistency inferred by the trained XGBoost machine. By analyzing patterns of mode transition, we also conclude that the maturation of cells affects the transition dynamics.