ArticleNature communications2025
RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Accurate RNA-Ligand Binding Site Prediction Based on a Multi-Channel Graph Neural Network.Interdisciplinary sciences, computational life sciences · 2026Article
- Accurately modeling RNase H-mediated antisense oligonucleotide efficacy.Molecular therapy. Nucleic acids · 2026Article
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
- GRASSP: RNA language model-enhanced graph attention with adaptive gating for RNA-small molecule binding site prediction.Bioinformatics (Oxford, England) · 2026Article
- Deciphering the comprehensive relationship between 5' UTR and 3' UTR sequences with deep learning.Bioinformatics (Oxford, England) · 2026Article
- Intron retention in health and amyotrophic lateral sclerosis.Brain : a journal of neurology · 2026Review
- M2-PRNet: multi-scale and multi-modal learning for protein-RNA binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- DeepExoMir: A Reproducible RNA Language Model Framework for CLIP-Seq-Supported MicroRNA Target-Site Prioritization.International journal of molecular sciences · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.Nature methods · 2026Article
- Agentomics: an agentic system that autonomously develops novel state-of-the-art solutions for biomedical machine learning tasks.Bioinformatics (Oxford, England) · 2026Article
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Machine Learning for CRISPR-Based Diagnostics.International journal of molecular sciences · 2026Review
- Benchmarking reveals the superiority of nucleic acid foundation models in predicting lncRNA coding potential.Genome biology · 2026Article
- 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
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- An RNA Language Model trained on sequence alone reveals the structural logic of Internal Ribosome Entry Sites.bioRxiv : the preprint server for biology · 2026Article
- Detecting and quantifying overparametrization in RNA language models with REDIAL.bioRxiv : the preprint server for biology · 2026Article
- Deep learning-driven prediction of on-target activity, off-target risk, and repair outcomes in CRISPR/Cas9: current landscape and multi-scale perspectives.Journal of translational medicine · 2026Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
While RNA has recently been recognized as an interesting small-molecule drug target, many challenges remain to be addressed before we take full advantage of it. This emphasizes the necessity to improve our understanding of its structures and functions. Over the years, sequencing technologies have produced an enormous amount of unlabeled RNA data, which hides a huge potential. Motivated by the successes of protein language models, we introduce RiboNucleic Acid Language Model (RiNALMo) to unveil the hidden code of RNA. RiNALMo is the largest RNA language model to date, with 650M parameters pre-trained on 36M non-coding RNA sequences from several databases. It can extract hidden knowledge and capture the underlying structure information implicitly embedded within the RNA sequences. RiNALMo achieves state-of-the-art results on several downstream tasks. Notably, we show that its generalization capabilities overcome the inability of other deep learning methods for secondary structure prediction to generalize on unseen RNA families.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.