ArticleBriefings in bioinformatics2025
Comprehensive benchmarking of large language models for RNA secondary structure prediction.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- RNAStructuromeDB: a transcriptome-wide database of predicted RNA secondary structures with integrated APIs for functional annotation and RNA-targeted drug discovery.NAR genomics and bioinformatics · 2026Article
- Exploring Secondary Structure Predictions for RNA-Targeted Drug Discovery: Power and Challenges.Journal of chemical information and modeling · 2026Article
- Quantifying uncertainty in protein representations across models and tasks.Nature methods · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- R3J-AGNN: GNN-Based Prediction of Inter-Branch Angles in RNA Three-Way Junctions from Secondary Structure.Biology · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- RNA Structure and Its Function.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.bioRxiv : the preprint server for biology · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- LncRNA-Protein Interactions: A Key to Deciphering LncRNA Mechanisms.Biomolecules · 2025Review
- Enhanced RNA secondary structure prediction through integrative deep learning and structural context analysis.Nucleic acids research · 2025Article
- Multiple Confabulations Found in Bioinformatics Tasks Carried Out by Several Free Large Language Models.Current genomics · 2025Article
- Robust RNA secondary structure prediction with a mixture of deep learning and physics-based experts.Biology methods & protocols · 2025Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In recent years, inspired by the success of large language models (LLMs) for DNA and proteins, several LLMs for RNA have also been developed. These models take massive RNA datasets as inputs and learn, in a self-supervised way, how to represent each RNA base with a semantically rich numerical vector. This is done under the hypothesis that obtaining high-quality RNA representations can enhance data-costly downstream tasks, such as the fundamental RNA secondary structure prediction problem. However, existing RNA-LLM have not been evaluated for this task in a unified experimental setup. Since they are pretrained models, assessment of their generalization capabilities on new structures is a crucial aspect. Nonetheless, this has been just partially addressed in literature. In this work we present a comprehensive experimental and comparative analysis of pretrained RNA-LLM that have been recently proposed. We evaluate the use of these representations for the secondary structure prediction task with a common deep learning architecture. The RNA-LLM were assessed with increasing generalization difficulty on benchmark datasets. Results showed that two LLMs clearly outperform the other models, and revealed significant challenges for generalization in low-homology scenarios. Moreover, in this study we provide curated benchmark datasets of increasing complexity and a unified experimental setup for this scientific endeavor. Source code and curated benchmark datasets with increasing complexity are available in the repository: https://github.com/sinc-lab/rna-llm-folding/.
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