ArticleGenome biology2025
HydraRNA: a hybrid architecture based full-length RNA language model.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- 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
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- HydraRNA: a hybrid architecture based full-length RNA language model.Genome biology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundRNA, an essential component of the central dogma of molecular biology, plays versatile roles in all cellular processes. RNA large language models (LLMs) are emerging as powerful methods in RNA research to decipher its intricate network of function and regulation. However, previous RNA LLMs were based on the Transformer model and pre-trained on short segment of non-coding RNAs, which limits their general usability. Here we present the first full-length RNA foundation model, HydraRNA, which is based on a hybrid architecture of bidirectional state space model and multi-head attention mechanism.
resultsHydraRNA is pre-trained on a large amount of both protein-coding mRNAs and non-coding RNAs. Despite being pre-trained with the fewest parameters and the least GPU resources, HydraRNA learns better RNA representations and outperforms the existing foundation models on a variety of mRNA-related tasks, including coding/non-coding RNA classification, prediction of RNA secondary structure, RBP binding sites, splicing and polyadenylation sites, mRNA stability and translation efficiency. Furthermore, HydraRNA can accurately predict the effect of mutations and estimate the relative contributions of different mRNA regions to the RNA stability and translation.
conclusionsOur results demonstrate that the hybrid architecture outperforms the pure Transformer architectures in RNA language modeling. We anticipate that HydraRNA will enable dissecting the diverse properties of mRNA, accelerating the research of mRNA regulation and facilitating the optimal design of mRNA therapeutics.
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