用深度学习自动分类单分子荧光事件，无需人工干预。
Automated Model-Free Sorting of Single-Molecule Fluorescence Events Using a Deep Learning Based Hidden-State Model
- 基于隐状态模型的端到端框架，无需手动设阈值。
- 在平衡与非平衡系统中均表现稳定，支持动态切割过程分析。
- 适合研究单分子级生物动力学结构变化，提升可重复性。
单分子荧光检测可实现生物分子动态的高分辨率分析，但传统分析流程依赖人工且易受经验影响，限制了可扩展性和可重复性。现有深度学习方法虽部分自动化数据处理，但仍需手动设定阈值、复杂架构或大量标注数据。为此，本文提出DASH——一种完全自动化的轨迹分类、状态赋值与排序架构，无需用户输入。DASH在平衡与非平衡系统（如Cas12a介导的DNA切割）中均表现出鲁棒性能，验证了其在单分子荧光事件自动精细分类中的有效性。该方法对研究单分子水平上的生物动力学结构变化具有重要意义。
原文摘要 · Abstract (English)
Single-molecule fluorescence assays enable high-resolution analysis of biomolecular dynamics, but traditional analysis pipelines are labor-intensive and rely on users' experience, limiting scalability and reproducibility. Recent deep learning models have automated aspects of data processing, yet many still require manual thresholds, complex architectures, or extensive labeled data. Therefore, we present DASH, a fully streamlined architecture for trace classification, state assignment, and automatic sorting that requires no user input. DASH demonstrates robust performance across users and experimental conditions both in equilibrium and non-equilibrium systems such as Cas12a-mediated DNA cleavage. This paper proposes a novel strategy for the automatic and detailed sorting of single-molecule fluorescence events. The dynamic cleavage process of Cas12a is used as an example to provide a comprehensive analysis. This approach is crucial for studying biokinetic structural changes at the single-molecule level.
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