School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
Nat Comput Sci. 2024 Oct;4(10):761-772. doi: 10.1038/s43588-024-00703-7. Epub 2024 Oct 11.
Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biological and materials sciences. Deep-learning techniques have recently outperformed conventional statistical methods in anomalous diffusion recognition. However, deep-learning networks are typically trained by data with limited distribution, which inevitably fail to recognize unknown diffusion models and misinterpret dynamics when confronted with out-of-distribution (OOD) scenarios. In this work, we present a general framework for evaluating deep-learning-based OOD dynamics-detection methods. We further develop a baseline approach that achieves robust OOD dynamics detection as well as accurate recognition of in-distribution anomalous diffusion. We demonstrate that this method enables a reliable characterization of complex behaviors across a wide range of experimentally diverse systems, including nicotinic acetylcholine receptors in membranes, fluorescent beads in dextran solutions and silver nanoparticles undergoing active endocytosis.
异常扩散在理解分子水平动力学方面起着至关重要的作用，为生物和材料科学领域的分子相互作用、迁移状态和系统物理性质提供了有价值的见解。最近，深度学习技术在异常扩散识别方面的表现优于传统的统计方法。然而，深度学习网络通常通过分布有限的数据进行训练，这不可避免地导致其无法识别未知的扩散模型，并在面对分布外（OOD）情况时错误地解释动力学。在这项工作中，我们提出了一个用于评估基于深度学习的 OOD 动力学检测方法的通用框架。我们进一步开发了一种基线方法，该方法能够实现稳健的 OOD 动力学检测以及对分布内异常扩散的准确识别。我们证明，该方法能够可靠地表征广泛的实验多样性系统中的复杂行为，包括膜中的烟碱型乙酰胆碱受体、葡聚糖溶液中的荧光珠和主动内吞作用的银纳米颗粒。
