{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mei X"],"funding":["National Natural Science Foundation of China"],"pagination":["btaf527"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12640237"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["41(11)"],"pubmed_abstract":["<h4>Motivation</h4>Accurate prediction of RNA secondary structure remains challenging due to the presence of pseudoknots, long-range dependencies, and limited labeled data.<h4>Results</h4>We propose TVAE, a novel framework that integrates a Transformer encoder with a Variational Autoencoder (VAE). The Transformer captures global dependencies in the sequence, while the VAE models structural variability by learning a probabilistic latent space. Unlike deterministic models, TVAE generates diverse and biologically plausible secondary structures, enabling more comprehensive structure discovery. To obtain discrete predictions, we introduce GHA-Pairing, a fast and biologically constrained base-pairing algorithm. TVAE demonstrates strong generalization across different RNA families and achieves state-of-the-art performance on benchmark datasets, reaching an F1 score of 0.89 and 83% accuracy, surpassing existing methods by 10%. These results highlight the advantage of probabilistic modeling for RNA structure prediction and its potential to enhance biological insights.<h4>Availability and implementation</h4>Code and pretrained models are available at https://github.com/mei-rna/TVAE-RNA. The released version of the dataset and models can also be accessed via DOI: 10.5281/zenodo.16946114."],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["TVAE-RNA: ensemble-based RNA secondary structure prediction via transformer variational autoencoders."],"pmcid":["PMC12640237"],"funding_grant_id":["62072210"],"pubmed_authors":["Liu H","Mei X","Zhu Y","Zhang H","Zhao E","Li L"],"additional_accession":[]},"is_claimable":false,"name":"TVAE-RNA: ensemble-based RNA secondary structure prediction via transformer variational autoencoders.","description":"<h4>Motivation</h4>Accurate prediction of RNA secondary structure remains challenging due to the presence of pseudoknots, long-range dependencies, and limited labeled data.<h4>Results</h4>We propose TVAE, a novel framework that integrates a Transformer encoder with a Variational Autoencoder (VAE). The Transformer captures global dependencies in the sequence, while the VAE models structural variability by learning a probabilistic latent space. Unlike deterministic models, TVAE generates diverse and biologically plausible secondary structures, enabling more comprehensive structure discovery. To obtain discrete predictions, we introduce GHA-Pairing, a fast and biologically constrained base-pairing algorithm. TVAE demonstrates strong generalization across different RNA families and achieves state-of-the-art performance on benchmark datasets, reaching an F1 score of 0.89 and 83% accuracy, surpassing existing methods by 10%. These results highlight the advantage of probabilistic modeling for RNA structure prediction and its potential to enhance biological insights.<h4>Availability and implementation</h4>Code and pretrained models are available at https://github.com/mei-rna/TVAE-RNA. The released version of the dataset and models can also be accessed via DOI: 10.5281/zenodo.16946114.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-06T03:13:38.47Z","creation":"2026-06-06T03:06:34.157Z"},"accession":"S-EPMC12640237","cross_references":{"pubmed":["40981507"],"doi":["10.1093/bioinformatics/btaf527"]}}