ArticleBioinformatics advances2022
Prediction of RNA-protein interactions using a nucleotide language model.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed, 66 citations in OpenAlex.
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Structure-aware graph learning predicts RNA editability across tissues and species.Research square · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- RNA Large Language Models in Virology.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- RNA-binding proteins connect Exon usage to the chromatin.NAR genomics and bioinformatics · 2025Article
- mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset.Nature communications · 2025Article
- Fine-tuning a sentence transformer for DNA.BMC bioinformatics · 2025Article
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- Article
- Predicting RNA Structure Utilizing Attention from Pretrained Language Models.Journal of chemical information and modeling · 2025Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.mSystems · 2025Article
- RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.BMC biology · 2025Article
- Article
- Review
- Emerging RNA-centric technologies to probe RNA-protein interactions: importance in decoding the life cycle of positive sense single strand RNA viruses and antiviral discovery.Frontiers in cellular and infection microbiology · 2025Review
- A review on the applications of Transformer-based language models for nucleotide sequence analysis.Computational and structural biotechnology journal · 2025Review
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: 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. Results: 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. Availability and implementation: 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/)]. Supplementary information: Supplementary data are available at
Identifiers
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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.