Biophysics Program, Stanford University, Stanford, California, USA.
Chemistry Department, Stanford University, Stanford, California, USA.
Phys Rev E. 2018 Jun;97(6-1):062412. doi: 10.1103/PhysRevE.97.062412.
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged covariate models to study such systems, linearity assumptions can limit the compression of inherently nonlinear dynamics into just a few characteristic components. Recent work in the field of deep learning has led to the development of the variational autoencoder (VAE), which is able to compress complex datasets into simpler manifolds. We present the use of a time-lagged VAE, or variational dynamics encoder (VDE), to reduce complex, nonlinear processes to a single embedding with high fidelity to the underlying dynamics. We demonstrate how the VDE is able to capture nontrivial dynamics in a variety of examples, including Brownian dynamics and atomistic protein folding. Additionally, we demonstrate a method for analyzing the VDE model, inspired by saliency mapping, to determine what features are selected by the VDE model to describe dynamics. The VDE presents an important step in applying techniques from deep learning to more accurately model and interpret complex biophysics.
通常，对时变化学和生物物理系统的分析会产生高维时间序列数据，对于这些数据，很难解释哪些单个特征最为显著。虽然我们小组和其他小组最近的工作已经证明了时滞协变量模型在研究此类系统中的效用，但线性假设可能会限制将固有的非线性动力学压缩为仅几个特征分量。深度学习领域的最新工作导致了变分自动编码器（VAE）的发展，该方法能够将复杂数据集压缩为更简单的流形。我们提出使用时滞 VAE 或变分动力学编码器（VDE）将复杂的非线性过程简化为单个嵌入，以高度保真度来描述基础动力学。我们展示了 VDE 如何在各种示例中捕获重要的动力学，包括布朗动力学和原子蛋白折叠。此外，我们还展示了一种基于显着性映射的分析 VDE 模型的方法，以确定 VDE 模型选择哪些特征来描述动力学。VDE 是将深度学习技术应用于更准确地建模和解释复杂生物物理学的重要步骤。
