预训练的大型语言模型的编码反映了人类心理特征的遗传架构
Bohan Xu1,2, Nick Obradovich1, Wenjie Zheng3
1Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.
medRxiv : the preprint server for health sciences
|April 8, 2025
概括
预先训练好的大型语言模型 (LLM) 惊人地捕获了心理健康特征中的遗传相关性,比描述更好地将疾病名称与遗传关系对齐.
科学领域:
- 计算生物学是一种计算生物学.
- 遗传学 是一个遗传学.
- 人工智能的人工智能是人工智能.
背景情况:
- 大型语言模型 (LLM) 在生物医学研究中越来越多地使用.
- 它们的内部运作在很大程度上是不透明的,需要人工的基准.
- 了解复杂生物数据的LLM编码至关重要.
研究的目的:
- 调查预训练的LLM在多大程度上编码与心理健康相关的遗传架构.
- 评估LLM在理解表型数据中的遗传相关性方面的有用性.
- 为了比较精神疾病名称与诊断描述的基因对齐.
主要方法:
- 使用预训练的大型语言模型,没有微调.
- 分析了经过验证的神经病症问卷中的项目之间的遗传相关性.
- 对比了精神疾病名称和诊断描述与遗传关系的对齐.
主要成果:
- 预先训练有素的LLM解释了神经病症问卷项目的遗传相关性高达51%.
- 精神疾病名称与遗传关系的结合比描述性文本更好.
- 发现LLM编码反映了潜在的遗传架构.
结论:
- 预先训练的LLM具有模拟遗传架构的固有能力.
- 在心理健康方面,LLM显示了表型验证和分类学改进的潜力.
- 使用LLM集成文本和遗传数据可以推进心理健康研究.
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