mlsanityData to 7 Oct 2026

Large Language Models in Bioinformatics: A Survey

arXiv 2503.04490 ↗First posted 6 Mar 2025cs.CL q-bio.GNcited by 6

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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

  1. 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
  2. 2023HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolutioncited by 187
  3. 2022ProGen2: Exploring the Boundaries of Protein Language Modelscited by 156
  4. 2023DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomecited by 139
  5. 2025Neuro-Symbolic AI in 2024: A Systematic Reviewcited by 36
  6. 2024RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Taskscited by 31
  7. 2021Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Designcited by 23
  8. 2024Progress and Opportunities of Foundation Models in Bioinformaticscited by 13
  9. 2023A Cognitive Stimulation Dialogue System with Multi-source Knowledge Fusion for Elders with Cognitive Impairmentcited by 10
  10. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
  11. 2024RNA-GPT: Multimodal Generative System for RNA Sequence Understandingcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.