Deep learning-assisted analysis of single molecule dynamics from liquid-phase electron microscopy,Chemical Communications

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Deep learning-assisted analysis of single molecule dynamics from liquid-phase electron microscopy
Chemical Communications ( IF 4.3 ) Pub Date : 2023-01-17 , DOI: 10.1039/d2cc05354c
Bin Cheng ₁ , Enze Ye _{2,

3} , He Sun ₂ , Huan Wang ₁

Affiliation

We apply U-Net and UNet++ to analyze single-molecule movies obtained from liquid-phase electron microscopy. Neural networks allow full automation, and high throughput analysis of these low signal-to-noise ratio images, while achieving higher segmentation accuracy, and avoiding subjective errors as compared to the conventional threshold methods. The analysis enables the quantification of transient dynamics in chemical systems and the capture of rare intermediate states by resolving local conformational changes within a single molecule.

中文翻译：

液相电子显微镜对单分子动力学的深度学习辅助分析

我们应用 U-Net 和 UNet++ 来分析从液相电子显微镜获得的单分子电影。与传统的阈值方法相比，神经网络允许对这些低信噪比图像进行完全自动化和高吞吐量分析，同时实现更高的分割精度，并避免主观错误。该分析能够量化化学系统中的瞬态动力学，并通过解析单个分子内的局部构象变化来捕获罕见的中间状态。

更新日期：2023-01-17

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