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, CA, USA.
Nature methods
|November 28, 2024
概括
大型语言模型 (LLM) 可以通过识别基因功能来协助功能基因学. GPT-4显示出有前途的结果,准确评估精选和随机基因组的信心.
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:
- 功能性基因组学依赖于基因功能数据库,这些数据库往往不完整.
- 从奥米克数据中发现基因功能对于生物学见解至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 从基因组中识别共同的基因功能的能力.
- 评估LLM提供分子推理和确定功能的自信评分的能力.
主要方法:
- 五个LLM (GPT-4,GPT-3.5,Gemini Pro,Mixtral Instruct,Llama2 70b) 在经过精选的基因本体学基因集和来自OMIC数据的基因集群上进行了测试.
- 性能通过功能相似性与精选名称,随机基因组的信心评估准确性,特异性和基因覆盖率来衡量.
主要成果:
- 在73%的精选基因组中,GPT-4准确地暗示了功能,并且对正确的 (随机) 基因组 (87%) 显示了高可靠性.
- 其他LLM表现出可变函数恢复,并且经常对随机集有错误的信心.
- 对于omics数据集群,GPT-4在45%的案例中确定了具有高特异性和基因覆盖率的功能,得到可验证的理由和引用的支持.
结论:
- 实际上,LLM,特别是GPT-4,在功能基因组学和OMIC数据分析中显示出作为有价值的助手的潜力.
- GPT-4的自我信心评估是功能预测准确性的可靠指标.
- 在功能发现过程中,LLM提供了对传统方法的补充方法,增强了功能发现中的特异性和基因覆盖率.
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