Xiangyun Qiu

This paper develops a mixture of experts approach combining deep learning and physics-based models to improve RNA secondary structure prediction, achieving accurate in-distribution and robust out-of-distribution results.

This study evaluates the generalizability of de novo deep learning models for RNA secondary structure prediction across different sequence distributions and identifies limitations in their performance.