# Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings

arXiv: https://arxiv.org/abs/2006.06885 · HTML: https://mlsanity.com/paper/2006.06885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks](https://mlsanity.com/paper/2201.08932/) (2022)
- [BLIS-Net: Classifying and Analyzing Signals on Graphs](https://mlsanity.com/paper/2310.17579/) (2023)
- [Visualizing DNA reaction trajectories with deep graph embedding approaches](https://mlsanity.com/paper/2311.03409/) (2023)
- [ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings](https://mlsanity.com/paper/2311.03411/) (2023)

## Abstract (arXiv)

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.