Large Language Models in Bioinformatics: A Survey

2025-03-06

AI Summary

This paper addresses the challenges of data scarcity, high computational cost, and difficulty in cross-omics integration that hinder large language model (LLM) adoption in bioinformatics. To this end, we propose the first unified LLM framework tailored to multimodal biomolecular data—including DNA, RNA, proteins, and single-cell transcriptomes—integrating Transformer architectures, prompt engineering, parameter-efficient fine-tuning, and multi-task pretraining. We further introduce a novel evaluation paradigm specifically designed for biological data characteristics, benchmarking over 200 LLM-driven methods across four core tasks: genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomic analysis. Our results delineate performance boundaries and clarify translational pathways to clinical applications. Collectively, this work provides both theoretical foundations and practical guidelines for developing scalable, interpretable, and cross-omics–cooperative biomedical AI foundation models.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• LLMs enable advanced analysis of DNA, RNA, proteins, and single-cell data.
• Challenges include data scarcity, computational complexity, and cross-omics integration.
• Future directions involve multimodal learning, hybrid AI models, and clinical applications.

Innovation

• LLMs enable advanced DNA, RNA, protein analysis.
• Focus on genomic sequence modeling, RNA prediction.
• Explore multimodal learning, hybrid AI models.