ArticleNature machine intelligence2024
An interpretable RNA foundation model for exploring functional RNA motifs in plants.
Article in Nature machine intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Structure-driven function of plant lncRNAs: conserved RNA architectures in transcriptional and post-transcriptional regulation.RNA biology · 2026Review
- AI foundation models for RNA biology.RNA biology · 2026Review
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- ContiTE: continuous manifold MoE for few-shot cross-tissue mRNA translation efficiency prediction.Briefings in bioinformatics · 2026Article
- Deciphering the comprehensive relationship between 5' UTR and 3' UTR sequences with deep learning.Bioinformatics (Oxford, England) · 2026Article
- RNA-Lexis: a probabilistic algorithm using a non-parametric segmentation logic to detect meaningful sequences in RNA.Nucleic acids research · 2026Article
- Which chemical features are captured by ChemBERTa's attention?Journal of computer-aided molecular design · 2026Article
- RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design.Genome biology · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- RNA regulation in plants.Science China. Life sciences · 2026Review
- Benchmarking reveals the superiority of nucleic acid foundation models in predicting lncRNA coding potential.Genome biology · 2026Article
- Generalist biological artificial intelligence in modeling the language of life.Nature biotechnology · 2026Review
- De novo design of functional nucleic acids of aptamers.Nature computational science · 2026Article
- What does it take to learn the rules of RNA base pairing? A lot less than you may think.Communications biology · 2026Article
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Review
- From Structure to Function of Promoters and 5'UTRs in Maize.International journal of molecular sciences · 2026Review
- Computational understanding of non-coding RNA pairwise interactions.Frontiers in artificial intelligence · 2026Article
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8 authors.
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Abstract
The complex 'language' of plant RNA encodes a vast array of biological regulatory elements that orchestrate crucial aspects of plant growth, development and adaptation to environmental stresses. Recent advancements in foundation models (FMs) have demonstrated their unprecedented potential to decipher complex 'language' in biology. In this study, we introduced PlantRNA-FM, a high-performance and interpretable RNA FM specifically designed for plants. PlantRNA-FM was pretrained on an extensive dataset, integrating RNA sequences and RNA structure information from 1,124 distinct plant species. PlantRNA-FM exhibits superior performance in plant-specific downstream tasks. PlantRNA-FM achieves an F1 score of 0.974 for genic region annotation, whereas the current best-performing model achieves 0.639. Our PlantRNA-FM is empowered by our interpretable framework that facilitates the identification of biologically functional RNA sequence and structure motifs, including both RNA secondary and tertiary structure motifs across transcriptomes. Through experimental validations, we revealed translation-associated RNA motifs in plants. Our PlantRNA-FM also highlighted the importance of the position information of these functional RNA motifs in genic regions. Taken together, our PlantRNA-FM facilitates the exploration of functional RNA motifs across the complexity of transcriptomes, empowering plant scientists with capabilities for programming RNA codes in plants.
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