基于迭代方法检测分子轨迹中扩散行为突变点，提升单粒子追踪精度。
Bottom-up Iterative Anomalous Diffusion Detector (BI-ADD)
- 融合无监督与有监督学习，逐轮迭代定位扩散特性变化的帧。
- 在AnDi2挑战赛中验证，可准确识别不同扩散模式下的转折点。
- 适用于单个及多个分子轨迹分析，助力基础生物学研究。
近年来，具有不同扩散特性的短分子轨迹分割受到广泛关注，因其有助于研究粒子动态行为。过去十年中，机器学习方法在变点检测与轨迹分割任务中展现出显著潜力。本文提出一种新型迭代方法——底向迭代异常扩散检测器（BI-ADD），用于识别分子轨迹中的变点，即扩散行为发生变化的帧。本研究中轨迹遵循分数布朗运动模型，通过估计其扩散特性实现检测。BI-ADD结合无监督与有监督学习策略，可应用于个体分子轨迹分析，并拓展至多粒子追踪，这是基础生物学的重要挑战。我们在专为单粒子追踪设计的AnDi2 Challenge 2024框架下，对BI-ADD进行了多场景验证。该方法以Python实现，已开源供科研使用。
原文摘要 · Abstract (English)
In recent years, the segmentation of short molecular trajectories with varying diffusive properties has drawn particular attention of researchers, since it allows studying the dynamics of a particle. In the past decade, machine learning methods have shown highly promising results, also in changepoint detection and segmentation tasks. Here, we introduce a novel iterative method to identify the changepoints in a molecular trajectory, i.e., frames, where the diffusive behavior of a particle changes. A trajectory in our case follows a fractional Brownian motion and we estimate the diffusive properties of the trajectories. The proposed BI-ADD combines unsupervised and supervised learning methods to detect the changepoints. Our approach can be used for the analysis of molecular trajectories at the individual level and also be extended to multiple particle tracking, which is an important challenge in fundamental biology. We validated BI-ADD in various scenarios within the framework of the AnDi2 Challenge 2024 dedicated to single particle tracking. Our method is implemented in Python and is publicly available for research purposes.
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