对大型语言模型进行评估,以发现基因组功能
Mengzhou Hu1, Sahar Alkhairy2, Ingoo Lee1
1Department of Medicine, University of California San Diego, La Jolla, California, USA.
Research square
|October 4, 2023
概括
像GPT-4这样的大型语言模型 (LLM) 现在可以通过生成信息性的基因组名来协助功能基因组学. 与传统方法相比,这种方法提供了对基因功能的更具上下文和更全面的理解.
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.
- 人工智能的人工智能
背景情况:
- 传统的基因组分析依赖于不完整的,手工策划的数据库.
- 现有的方法往往缺乏生物背景,并与新发现作斗争.
研究的目的:
- 评估OpenAI的GPT-4大型语言模型 (LLM),用于生成基因功能假设.
- 与现有数据库相比,评估LLM生成的基因集合名称的实用性.
主要方法:
- 开发了一个GPT-4管道,用共识函数名称标记基因组,包括支持文本和引用.
- 对已知基因组的基因本体学 (GO) 进行基准GPT-4生成名称.
- 评估了GPT-4在基因组上的性能,这些基因组来源于'omics数据'.
主要成果:
- 在50%的案例中,GPT-4生成了与GO非常相似的名称,而在其他情况下则产生了更一般的概念.
- 对于'omics数据,GPT-4名称比标准基因组丰富更具信息性.
- 由GPT-4提供的支持性陈述和引文在很大程度上可以在人类审查后进行验证.
结论:
- 大型语言模型显示作为功能基因组学助理的希望.
- GPT-4可以快速合成常见的基因功能,改善生物背景和假设生成.
- 基于LLM的方法为现有的生物信息学工具提供了有价值的补充.
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