大模型正推动生物信息学变革，助力基因、蛋白与单细胞数据分析。
Large Language Models in Bioinformatics: A Survey
- 系统梳理大模型在基因组、蛋白质和单细胞数据中的应用进展
- 指出数据稀缺与跨组学整合仍是主要挑战
- 适合关注AI+生命科学交叉研究的学者与临床转化团队
大型语言模型（LLMs）正在重塑生物信息学，推动对DNA、RNA、蛋白质及单细胞数据的高级分析。本文系统综述了近期进展，重点涵盖基因序列建模、RNA结构预测、蛋白质功能推断以及单细胞转录组学。同时，讨论了数据稀缺、计算复杂度高和跨组学整合等关键挑战，并探索了多模态学习、混合人工智能模型与临床应用等未来方向。通过提供全面视角，本文强调了大模型在推动生物信息学与精准医学创新中的变革潜力。
原文摘要 · Abstract (English)
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.
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