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3D Nanoscale Tracking Data Analysis for Intracellular Organelle Movement using Machine Learning Approach

机译:使用机器学习方法进行细胞内细胞器运动的3D纳米级跟踪数据分析

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Tracking of intracellular organelle movement such as vesicle includes crucial information in biomedicine. To achieve more accurate three-dimensional localization of the target organelle, superresolution imaging microscopy and image processing methods have been developed and applied to many nanoscale tracking systems. Although such recent advances in microscopy imaging have enabled us to gather a tremendous amount of tracking data, the details of the movement including the interaction between cytoskeletons are not yet fully explained. In the present work, we suggest a machine learning approach to clarify the problem in tracking data analysis, as an initial trial to exploit artificial intelligence in distinguishing and classifying the detail features of the vesicle-cytoskeleton interactions.
机译:诸如小泡之类的细胞内细胞器运动的追踪包括生物医学中的关键信息。为了实现目标细胞器的更精确的三维定位,已经开发了超分辨率成像显微镜和图像处理方法,并将其应用于许多纳米级跟踪系统。尽管在显微镜成像方面的最新进展使我们能够收集大量跟踪数据,但运动的细节(包括细胞骨架之间的相互作用)尚未得到充分解释。在当前的工作中,我们建议一种机器学习方法来澄清跟踪数据分析中的问题,作为利用人工智能来区分和分类囊泡-细胞骨架相互作用的详细特征的初步试验。

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