Shifu: an integrated framework for deep learning of RNA secondary structure

Abstract

Deep learning has advanced RNA secondary-structure prediction by bypassing explicit energy rules to capture long-range dependencies, yet progress is limited less by model scale than by how structures are measured: single scores hide where and why models fail, and benchmark scores can reflect memorization of one dataset rather than genuine generalization. We address this with Shifu, a framework of three coupled parts. Shifu-Corpus is a leakage-audited dataset of 254123 sequences from six databases, with family-aware splits certified free of exact and near-duplicate leaks. The Shifu Trifecta scores a model on three axes (correctness, breadth across diverse RNAs, and whether its confidence can be trusted) rather than one number. Shifu-LMR, a family of compact RNA language models, serves as controlled experiments: changing the training corpus shifts accuracy by 0.13, and a 65-million-parameter model, Shifu-LMR-Nano, leads on correctness while running on a laptop. We release the dataset, code, and model backbones.