{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2(1)"],"submitter":["Yamada K"],"pubmed_abstract":["<h4>Motivation</h4>The accumulation of sequencing data has enabled researchers to predict the interactions between RNA sequences and RNA-binding proteins (RBPs) using novel machine learning techniques. However, existing models are often difficult to interpret and require additional information to sequences. Bidirectional encoder representations from transformer (BERT) is a language-based deep learning model that is highly interpretable. Therefore, a model based on BERT architecture can potentially overcome such limitations.<h4>Results</h4>Here, we propose BERT-RBP as a model to predict RNA-RBP interactions by adapting the BERT architecture pretrained on a human reference genome. Our model outperformed state-of-the-art prediction models using the eCLIP-seq data of 154 RBPs. The detailed analysis further revealed that BERT-RBP could recognize both the transcript region type and RNA secondary structure only based on sequence information. Overall, the results provide insights into the fine-tuning mechanism of BERT in biological contexts and provide evidence of the applicability of the model to other RNA-related problems.<h4>Availability and implementation</h4>Python source codes are freely available at https://github.com/kkyamada/bert-rbp. The datasets underlying this article were derived from sources in the public domain: [RBPsuite (http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/), Ensembl Biomart (http://asia.ensembl.org/biomart/martview/)].<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online."],"journal":["Bioinformatics advances"],"pagination":["vbac023"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9710633"],"repository":["biostudies-literature"],"pubmed_title":["Prediction of RNA-protein interactions using a nucleotide language model."],"pmcid":["PMC9710633"],"pubmed_authors":["Hamada M","Yamada K"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of RNA-protein interactions using a nucleotide language model.","description":"<h4>Motivation</h4>The accumulation of sequencing data has enabled researchers to predict the interactions between RNA sequences and RNA-binding proteins (RBPs) using novel machine learning techniques. However, existing models are often difficult to interpret and require additional information to sequences. Bidirectional encoder representations from transformer (BERT) is a language-based deep learning model that is highly interpretable. Therefore, a model based on BERT architecture can potentially overcome such limitations.<h4>Results</h4>Here, we propose BERT-RBP as a model to predict RNA-RBP interactions by adapting the BERT architecture pretrained on a human reference genome. Our model outperformed state-of-the-art prediction models using the eCLIP-seq data of 154 RBPs. The detailed analysis further revealed that BERT-RBP could recognize both the transcript region type and RNA secondary structure only based on sequence information. Overall, the results provide insights into the fine-tuning mechanism of BERT in biological contexts and provide evidence of the applicability of the model to other RNA-related problems.<h4>Availability and implementation</h4>Python source codes are freely available at https://github.com/kkyamada/bert-rbp. The datasets underlying this article were derived from sources in the public domain: [RBPsuite (http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/), Ensembl Biomart (http://asia.ensembl.org/biomart/martview/)].<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-21T23:08:34.311Z","creation":"2025-04-05T19:06:25.401Z"},"accession":"S-EPMC9710633","cross_references":{"pubmed":["36699410"],"doi":["10.1093/bioadv/vbac023"]}}