ERNIE-RNA: an RNA language model with structure-enhanced representations
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Abstract
Abstract With large amounts of unlabeled RNA sequences data produced by high-throughput sequencing technologies, pre-trained RNA language models have been developed to estimate semantic space of RNA molecules, which facilities the understanding of grammar of RNA language. However, existing RNA language models overlook the impact of structure when modeling the RNA semantic space, resulting in incomplete feature extraction and suboptimal performance across various downstream tasks. In this study, we developed a RNA pre-trained language model named ERNIE-RNA ( E nhanced R eprese n tations with base-pa i ring r e striction for RNA modeling) based on a modified BERT (Bidirectional Encoder Representations from Transformers) by incorporating base-pairing restriction with no MSA (Multiple Sequence Alignment) information. We found that the attention maps from ERNIE-RNA with no fine-tuning are able to capture RNA structure in the zero-shot experiment more precisely than conventional methods such as fine-tuned RNAfold and RNAstructure, suggesting that the ERNIE-RNA can provide comprehensive RNA structural representations. Furthermore, ERNIE-RNA achieved SOTA (state-of-the-art) performance after fine-tuning for various downstream tasks, including RNA structural and functional predictions. In summary, our ERNIE-RNA model provides general features which can be widely and effectively applied in various subsequent research tasks. Our results indicate that introducing key knowledge-based prior information in the BERT framework may be a useful strategy to enhance the performance of other language models.
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AI-Driven Insights into RNA Sequence Analysis Landscape
Asim, M. N., Ahmed, S., & Dengel, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-90454-7_4
Applications of large‐scale artificial intelligence models in bioinformatics
Li, M., Shang, Q., Dong, Z., You, Z. S., Zhang, L., & Xiao, M. (2025) · Quantitative Biology · doi:10.1002/qub2.70026
Spatial-neighbour encoding enables fast RNA 3D structure searchPreprint
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Self-citationRiboGRAM learns multi-scale non-coding RNA grammar for structural and regulatory inferencePreprint
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- Authors
- Yin, W., Zhang, Z., Zhang, S., He, L., Zhang, R., Jiang, R., Liu, G., Wang, J., Zhang, X., Qin, T., & Xie, Z.
- Affiliations
- Beijing National Research Center for Information Science and Technology (CN)
- Microsoft (United States) (US)
- Beijing Tongren Hospital (CN)
- Corresponding author
- Zhang, Xuegong
- Qin, Tao
- Xie, Zhen
- ORCID
- Venue
- Nature Communications
- Published
- 2025-11-18
- Volume
- 16
- Issue
- 1
- Pages
- 10076-10076
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12627772
- OpenAlex
- W4416334285
- Cited by
- 55
- References
- 67
- Citations by year
- 2026: 32 · 2025: 20 · 2024: 3
- FWCI
- 15.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (No. 32341014 [Z.X.])
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- RNA language models, RNA structure prediction, RNA function prediction, attention maps, BERT, pre-trained language models, RNA folding
- MeSH terms
- Nucleic Acid Conformation, RNA, Computational Biology, Base Pairing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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