Shifu, a framework of three coupled parts, scores a model on three axes (correctness, breadth across diverse RNAs, and whether its confidence can be trusted) rather than one number, and releases the dataset, code, and model backbones.
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.
Crucially, the analysis reveals that the backbone encoder becomes self-contained post-training: the memory module facilitates the internalization of motif semantics into the model parameters, allowing the backbone to retain performance advantages even when the memory is detached during inference.
Xiang-Yu Ji, Xin Wang, Yang Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
The state of the art in RNA structure prediction is reviewed, covering key training datasets, community benchmarks, and the performance of current models, as well as perspectives on integrating other data modalities, such as chemical probing signals and RNA modifications, as well as the emerging role of generative mode...
Lambert Moyon, Annalisa Marsico· Current Opinion in Structura...· 0 citations
SPIRAL is presented, a layer-wise SAE analysis of BiRNA-BERT, a layer-wise SAE analysis of RNA language models where byte-pair tokenization breaks the one-token-one-nucleotide correspondence that nucleotide-level attribution assumes.
M. Hossain, MD. Roqunuzzaman Sojib, Md Toki Tahmid et al.· bioRxiv· 0 citations
A large and diverse dataset comprising paired RNA sequences and their corresponding secondary structures is developed, and SincFold exhibited improved performance on a set of previously unseen RNA families, enhancing its capability to predict accurate de novo RNA secondary structures.
Conner J. Langeberg, Taehan Kim, Roma Nagle et al.· RNA: A publication of the RN...· 0 citations
The results show that examining branching configurations optimal modulo the branching energy provides structural information beyond the standard MFE prediction, and the proposed energy-filtering approach yields a compact set of alternative structural hypotheses that can complement Boltzmann sampling and provide a pract...
Yuta Hozumi, Svetlana Poznanovic, Christine E. Heitsch· Biophysical Journal· 0 citations
A cheap density prior is introduced over natural protein activations and keeps only the candidates that remain typical under it, a training-free post-hoc step the authors call Mahalanobis filtering that improves both the property score and the structural plausibility of the sequences it keeps at negligible cost, withou...
Shuibai Zhang, Xin-Chi Liu, Fred Zhangzhi Peng et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
