ViDa：利用生物物理 informed 深度图嵌入可视化DNA杂交轨迹
ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings
AI总结：
本研究提出ViDa可视化方法，结合散射变换、变分自编码器与非线性降维，加入生物物理领域监督损失，实现DNA杂交反应轨迹的高质量二维嵌入，优于现有方法，可揭示反应机制。
摘要：
可视化工具可帮助合成生物学家和分子程序员理解核酸反应的复杂反应路径，这类反应可被设计用于多种潜在应用，且能用连续时间马尔可夫链（CTMC）建模。本文提出ViDa，一种用于DNA反应轨迹的新型可视化方法，它采用CTMC模型底层二级结构状态空间的二维嵌入。为此，我们整合了二级结构邻接矩阵的散射变换、变分自编码器以及一种非线性降维方法。我们在训练损失中加入了捕捉热力学和动力学特征的领域特定监督项。我们在两个已被充分研究的DNA杂交反应上评估了ViDa。结果表明，相较于DNA状态空间可视化的现有最优方法，领域特定特征带来了显著的质量提升，成功分离了不同折叠路径，从而为主要反应机制提供了有用见解。
英文摘要：
Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization approach for DNA reaction trajectories that uses a 2D embedding of the secondary structure state space underlying the CTMC model. To this end, we integrate a scattering transform of the secondary structure adjacency, a variational autoencoder, and a nonlinear dimensionality reduction method. We augment the training loss with domain-specific supervised terms that capture both thermodynamic and kinetic features. We assess ViDa on two well-studied DNA hybridization reactions. Our results demonstrate that the domain-specific features lead to significant quality improvements over the state-of-the-art in DNA state space visualization, successfully separating different folding pathways and thus providing useful insights into dominant reaction mechanisms.
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