将LLM衍生信息纳入基因组学应用的假设测试中
Jordan G Bryan1, Hongqian Niu1, Didong Li1
1Department of Biostatistics, The University of North Carolina at Chapel Hill.
bioRxiv : the preprint server for biology
|July 14, 2025
概括
这项研究将大型语言模型 (LLM) 集成到基因组学假设测试中. 来自LLM的基因嵌入增强了基因组学分析中的统计能力,优于传统方法.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 人工智能在生物学中的应用
背景情况:
- 在基因组学研究中,统计假设测试至关重要.
- 大型语言模型 (LLM) 提供了新的方法来提取生物信息.
- 将LLM洞察力集成到基因组学分析中仍然是一个未经探索的领域.
研究的目的:
- 开发将LLM信息纳入基因组学假设测试的策略.
- 利用LLM的基因嵌入来提高基因组学研究中的统计能力.
- 引入一种新的频率学和贝叶斯学 (FAB) 框架来进行假设测试.
主要方法:
- 从文本输入中使用OpenAI的GPT-3.5模型生成基因嵌入.
- 分析了基因嵌入的主要子空间以识别生物信号.
- 在频率主义和贝叶斯式 (FAB) 框架内开发了三种假设测试,以LLM嵌入为指导.
主要成果:
- 发现基因组学数据集中的生物信号位于LLM衍生基因嵌入的主要子空间附近.
- 与古典方法相比,拟议的FAB假设测试显示了较大的统计能力.
- 在三个不同的现实世界基因组学数据集中成功应用了FAB测试.
结论:
- 来自LLM的基因嵌入为基因组学假设测试提供了有价值的预先信息.
- FAB框架有效地整合了LLM洞察力,以增强基因组学中的统计能力.
- 这种方法为推进基因组数据分析的统计方法提供了一个有希望的方向.
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