Scalable Deep Learning for RNA Secondary Structure Prediction
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Abstract (arXiv)
The field of RNA secondary structure prediction has made significant progress with the adoption of deep learning techniques. In this work, we present the RNAformer, a lean deep learning model using axial attention and recycling in the latent space. We gain performance improvements by designing the architecture for modeling the adjacency matrix directly in the latent space and by scaling the size of the model. Our approach achieves state-of-the-art performance on the popular TS0 benchmark dataset and even outperforms methods that use external information. Further, we show experimentally that the RNAformer can learn a biophysical model of the RNA folding process.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 47 references that are in the map. First posted on arXiv 14 Jul 2023.
Builds on
9 of 47 references.
References · 9
- 2017Attention Is All You Needcited by 59,932
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- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,128
- 2019Axial Attention in Multidimensional Transformerscited by 350
- 2020RNA Secondary Structure Prediction By Learning Unrolled Algorithmscited by 24
- 2017Designing RNA Secondary Structures is Hardcited by 15
- 2019LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam searchcited by 10
- 2022Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Designcited by 7
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.