ReviewBioinformatics advances2023
Applications of transformer-based language models in bioinformatics: a survey.
Review in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
73 citing papers in PubMed.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Optimizing protein tokenization: reduced amino acid alphabets for efficient and accurate protein language models.Bioinformatics (Oxford, England) · 2026Article
- A pretrained biomedical large language model for Persian biomedical text mining.Scientific reports · 2026Article
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Decoding causal m6A: a bioinformatics roadmap for psychiatric disorders.Briefings in bioinformatics · 2026Review
- A Multi-Step Computational Workflow for Screening and Prioritizing SHP2-Binding Molecules.Pharmaceuticals (Basel, Switzerland) · 2026Article
- EPInformer: scalable and integrative prediction of gene expression from promoter-enhancer sequences with multimodal epigenomic profiles.Nature communications · 2026Article
- CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A novel transformer-based platform for the prediction and design of biosynthetic gene clusters for (un)natural products.PLoS computational biology · 2026Article
- A multicenter deep learning framework integrating radiomics and vision transformers for comprehensive ovarian tumor analysis from ultrasound imaging.European journal of medical research · 2026Article
- ASAS-NANP symposium: mathematical modeling in animal nutrition: review of time series techniques and machine learning models for advancing livestock production.Journal of animal science · 2026Review
- Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Charting the virosphere: computational synergies of AI and bioinformatics in viral discovery and evolution.Journal of virology · 2025Review
- Non-invasive anemia detection from conjunctiva and sclera images using vision transformer with attention map explainability.Scientific reports · 2025Article
- From Sequence to Response: AI-Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Predicting pathological subtypes of pure ground-glass nodules using Swin Transformer deep learning model.Insights into imaging · 2025Article
- Streamline automated biomedical discoveries with agentic bioinformatics.Briefings in bioinformatics · 2025Review
- Drug repurposing for Alzheimer's disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph.BioData mining · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- ESM2_AMP: an interpretable framework for protein-protein interactions prediction and biological mechanism discovery.Briefings in bioinformatics · 2025Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: The transformer-based language models, including vanilla transformer, BERT and GPT-3, have achieved revolutionary breakthroughs in the field of natural language processing (NLP). Since there are inherent similarities between various biological sequences and natural languages, the remarkable interpretability and adaptability of these models have prompted a new wave of their application in bioinformatics research. To provide a timely and comprehensive review, we introduce key developments of transformer-based language models by describing the detailed structure of transformers and summarize their contribution to a wide range of bioinformatics research from basic sequence analysis to drug discovery. While transformer-based applications in bioinformatics are diverse and multifaceted, we identify and discuss the common challenges, including heterogeneity of training data, computational expense and model interpretability, and opportunities in the context of bioinformatics research. We hope that the broader community of NLP researchers, bioinformaticians and biologists will be brought together to foster future research and development in transformer-based language models, and inspire novel bioinformatics applications that are unattainable by traditional methods. Supplementary information: Supplementary data are available at
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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.