在生物信息学中对基因组语言模型进行了全面的调查
Liyuan Shu1, Jiao Tang1, Xiaoyu Guan1
1College of Artificial Intelligence, Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, No. 29 Jiangjun Avenue, Jiangning District, Nanjing, Jiangsu Province 211106, China.
Briefings in bioinformatics
|January 15, 2026
概括
基因组语言模型 (gLMs) 适应大型语言模型进行DNA/RNA分析,揭示基因组语法和调控相互作用. 本综述调查了gLM方法,应用和未来基因组研究的挑战.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 大型语言模型 (LLM) 在自然语言处理方面表现出色,捕捉复杂的语义和远程依赖.
- 基因组语言模型 (gLMs) 将LLM扩展到生物序列 (DNA/RNA),将它们视为解读基因组语法和监管元素的文本.
研究的目的:
- 审查gLMs在基因组序列表征中的必要性和能力.
- 调查当前的gLM架构,代币化策略,预训练方法和数据集.
- 分析基因组学中的评估方法和下游应用.
主要方法:
- 关于gLM架构的综合文献评论 (变压器,海,国家空间模型).
- 对序列令牌化策略和预训练方法的调查.
- 评估范式的分析 (监督,零射击,少数射击) 和微调.
主要成果:
- 对于基因组序列分析而言,gLM比传统的深度学习具有优势.
- 不同的gLM架构和代币化方法在各种基因组任务中显示出前景.
- 既定的预培训策略和数据集有助于gLM开发.
结论:
- gLMs代表了分析基因组序列的重大进步.
- 关键的挑战包括数据稀缺性,可解释性和计算成本.
- 提出了一份路线图,指导未来对基因组语言建模的研究.
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