Eterna Participants

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

The paper designs compact RNA sensors for complex functions of multiple inputs using crowdsourced designs and an algorithm, achieving applications like tuberculosis diagnosis.

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.

The paper describes the design and characterization of mini-PTC 1.1, a minimal ribosomal PTC RNA optimized through community science and RNA structural analysis.

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.

A crowdsourced RNA design approach discovers efficient, reversible molecular sensors that couple small molecule inputs to protein binding and fluorogenic outputs.

The paper introduces EternaBrain, an automated RNA design algorithm trained on player moves from an RNA videogame, achieving performance comparable to human players on benchmark puzzles.

This paper evaluates a recurrent neural network model, Ribonet, for learning RNA biophysics from high-throughput data, showing it can predict RNA interactions and design functional RNAs but requires more diverse datasets for accurate biophysical modeling.