期刊
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
卷 -, 期 -, 页码 4519-4528出版社
IEEE
DOI: 10.1109/BigData50022.2020.9378305
关键词
graph embedding; molecular structures; self-supervised learning
类别
资金
- NIH [R01GM135929, R01GM130847]
- IVADO [PRF-2019-3583139727]
- Chan-Zuckerberg Initiative grants [182702, CZF2019-002440]
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.
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