Scalable Deep Learning for RNA Secondary Structure Prediction
Jörg K. H. Franke Frederic Runge Frank Hutter
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.