论文标题
用深图嵌入发现RNA二级结构的折叠景观
Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings
论文作者
论文摘要
最近在几何深度学习的新兴领域中，生物分子图分析引起了很多关注。在这里，我们专注于以暴露有意义的关系及其之间的变化方式来组织生物分子图。我们提出了一个几何散射自动编码器（GSAE）网络，用于学习这种图形嵌入。我们的嵌入网络首先使用最近提出的几何散射变换来提取丰富的图形特征。然后，它利用半监督的变异自动编码器提取低维嵌入，该嵌入在这些特征中保留信息，以预测分子特性并表征图。我们表明，GSAE通过结构和能量组织RNA图，从而准确地反映了可动的RNA结构。同样，该模型是生成的，可以采样新的折叠轨迹。
Biomolecular graph analysis has recently gained much attention in the emerging field of geometric deep learning. Here we focus on organizing biomolecular graphs in ways that expose meaningful relations and variations between them. We propose a geometric scattering autoencoder (GSAE) network for learning such graph embeddings. Our embedding network first extracts rich graph features using the recently proposed geometric scattering transform. Then, it leverages a semi-supervised variational autoencoder to extract a low-dimensional embedding that retains the information in these features that enable prediction of molecular properties as well as characterize graphs. We show that GSAE organizes RNA graphs both by structure and energy, accurately reflecting bistable RNA structures. Also, the model is generative and can sample new folding trajectories.
