Brent M. Znosko

The paper introduces a large dataset of cryo-EM RNA motifs and benchmarks machine learning models for structure prediction.

This paper presents nearest neighbor parameters for estimating RNA folding stability with pseudouridine, derived from optical melting experiments, and demonstrates their application in modeling RNA secondary structures.

This paper develops new RNA folding nearest neighbor parameters for sequences containing 1-methyl-pseudouridine, showing it stabilizes RNA folding and improves tRNA ensemble modeling.

This study benchmarks graph neural networks for RNA secondary structure prediction, showing they outperform traditional energy-based methods.