Large Language Models in Bioinformatics: A Survey
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Abstract (arXiv)
Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss several key challenges, including data scarcity, computational complexity, and cross-omics integration, and explore future directions such as multimodal learning, hybrid AI models, and clinical applications. By offering a comprehensive perspective, this paper underscores the transformative potential of LLMs in driving innovations in bioinformatics and precision medicine.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 11 of its 81 references that are in the map. First posted on arXiv 6 Mar 2025.
Builds on
11 of 81 references.
References · 11
- 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
- 2023HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolutioncited by 187
- 2022ProGen2: Exploring the Boundaries of Protein Language Modelscited by 156
- 2023DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomecited by 139
- 2025Neuro-Symbolic AI in 2024: A Systematic Reviewcited by 36
- 2024RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Taskscited by 31
- 2021Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Designcited by 23
- 2024Progress and Opportunities of Foundation Models in Bioinformaticscited by 13
- 2023A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairmentcited by 10
- 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
- 2024RNA-GPT: Multimodal Generative System for RNA Sequence Understandingcited by 8
Built on by
Citing papers per year: 2025: 4; 2026: 2.
6 papers.
- 2025Read Before You Think: Mitigating LLM Comprehension Failures with Step-by-Step Readingcited by 8
- 2025A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluationcited by 3
- 2026A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairmentcited by 1
- 2025Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generationcited by 0
- 2025DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Designcited by 0
- 2026Evaluating Agentic Bioinformatics through Function, Evidence, and Validationcited by 0
- 2026Evaluating Agentic Bioinformatics through Function, Evidence, and Validation30 Jul 2026
- 2026A Principle-Driven Adaptive Policy for Group Cognitive Stimulation Dialogue for Elderly with Cognitive Impairment1 Mar 2026
- 2025A Multimodal, Multilingual, and Multidimensional Pipeline for Fine-grained Crowdsourcing Earthquake Damage Evaluation3 Jun 2025
- 2025DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design18 May 2025
- 2025Read Before You Think: Mitigating LLM Comprehension Failures with Step-by-Step Reading13 Apr 2025
- 2025Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation1 Apr 2025