ReviewComputational and structural biotechnology journal2025
A review on the applications of Transformer-based language models for nucleotide sequence analysis.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.RNA biology · 2026Article
- An enzyme-specific protein language model for catalytic property prediction.Nature communications · 2026Article
- Liquid Biopsy in Colorectal Cancer: Future Perspectives Through the Lens of Artificial Intelligence-A Comprehensive Review of Novel Literature.International journal of molecular sciences · 2026Review
- Deep Learning-Based Identification of Pathogenicity Genes inPlants (Basel, Switzerland) · 2026Article
- BlendSplice: A Frequency-Blended Generative Framework forComputational and structural biotechnology journal · 2026Article
- Peripheral Blood TCR Clonotype Diversity as a Biomarker for Colorectal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- Transformer-based classification and interpretability of NR3C1 expression patterns in OSCC: Metabolic adaptation insights.Journal of oral biology and craniofacial researchArticle
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Authors and funding
4 authors.
Funding
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Abstract
Transformer-based language models are making an impact in the field of Natural Language Processing (NLP). As relevant parallels can be drawn between biological sequences and natural languages, the models used in NLP can be easily extended and adapted for applications in bioinformatics. This paper introduces the recent developments of Transformer-based models in the context of nucleotide sequences. We have reviewed and analysed a large number of application-based papers on this subject, giving evidence of the main characterizing features and to the different approaches that may be adopted to customize such powerful computational machines. Besides discussing what Transformers do and may do for the analysis of biological sequences, we also provide an overview of what Transformers are and why they work. We believe this review will help the scientific community in understanding the application of Transformer-based language models to nucleotide sequences, and that will motivate the readers to build on idea of Transformers as well as the discussed methodologies to tackle different problems in the field of bioinformatics.
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Registered trials
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