Jonathan Romano

This paper demonstrates that deep learning can accurately design RNA pseudoknots, achieving results comparable to human experts in a competition.

This study uses community science and RNA structure prediction to design splice-modulating ASOs that enhance exon 16 splicing in a hemophilia A model.

The paper introduces RibonanzaNet, a deep learning model trained on crowdsourced RNA chemical mapping data to predict RNA structure and related properties.

This study uses community science and experimental screening to design mutant ribosomes with improved protein synthesis and cell growth in vitro and vivo.

This study develops PERSIST-seq and In-line-seq to optimize mRNA stability and translation, finding that in-cell stability is more critical than ribosome load for protein output.

The paper updates the Eterna100 RNA design benchmark to work with ViennaRNA 2 and shows that design performance depends on the folding engine used.