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  3. Ernie-rna: An Rna Language Model With Structure-enhanced Representations.
  1. Home
  3. Ernie-rna: An Rna Language Model With Structure-enhanced Representations.

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ERNIE-RNA: an RNA language model with structure-enhanced representations.

Weijie Yin1, Zhaoyu Zhang1, Shuo Zhang1

  • 1MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and Systems Biology, Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.

Nature Communications
|November 18, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

ERNIE-RNA, a novel RNA language model, integrates structural information for enhanced RNA sequence analysis. This model achieves state-of-the-art performance in predicting RNA structure and function.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Existing RNA language models (RLMs) often neglect crucial structural information in RNA sequences.
  • This oversight leads to incomplete feature extraction and suboptimal performance on various biological tasks.

Purpose of the Study:

  • To introduce ERNIE-RNA, an enhanced RNA language model incorporating base-pairing restrictions.
  • To demonstrate ERNIE-RNA's superior capability in capturing RNA structural features and improving downstream task performance.

Main Methods:

  • ERNIE-RNA is built upon a modified Bidirectional Encoder Representations from Transformers (BERT) architecture.
  • The model utilizes attention mechanisms to capture RNA structural information, enabling zero-shot prediction.

Main Results:

  • ERNIE-RNA's attention maps show enhanced ability to capture RNA structural features compared to traditional methods like RNAfold and RNAstructure.
  • Fine-tuned ERNIE-RNA achieves state-of-the-art results in RNA structure and function prediction tasks.
  • The model demonstrates natural development of comprehensive RNA architecture representations during pre-training.

Conclusions:

  • ERNIE-RNA offers versatile features for diverse RNA research applications.
  • Integrating knowledge-based priors into BERT-like frameworks can significantly improve language model performance for biological sequences.