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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.

|September 21, 2023

概括

大型语言模型 (LLM) 可以通过从基因功能中识别基因功能来帮助功能基因学.

科学领域:

功能性基因组学是一种功能性基因组学.
生物信息学是一种生物信息学.
计算生物学是一种计算生物学.

背景情况:

目前的基因功能数据库不完整,限制了功能基因组学.
基因组分析需要准确识别常见的生物功能.
评估大型语言模型 (LLM) 对基因组分析的实用性至关重要.

研究的目的:

评估五种大型语言模型 (LLM) 在从基因组中识别共同的生物功能方面的表现.
评估LLM提供支持的理由,引文和确定功能的信心评估的能力.
为了确定LLM是否可以发现超越传统丰富方法的新基因功能.

主要方法:

在基因本体学中对五个LLM (GPT-4,Gemini-Pro,Mixtral-Instruct,Llama2-70b) 与正规和随机基因组进行了基因基因基因基因组的基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因基因.
根据函数命名的准确性,信心评估和从"omics数据中识别新功能来评估LLM性能.
独立审查通过LLM识别的新功能,以验证它们的生物相关性并区分它们与幻觉.

主要成果:

GPT-4在识别正规基因组的功能方面获得了高可靠性 (73%),并正确地为随机组分配了零可靠性.
GPT-4在'omics数据基因组中发现了新的,可验证的功能,而不是通过经典丰富 (32%的病例).
双子星-Pro和Mixtral-Instruct显示命名能力,但在随机集上表现出虚假的信心;Llama2-70b的整体表现不佳.

结论:

实际上,LLM,特别是GPT-4,作为功能基因组学和"omics"数据分析的工具,显示出显著的前景.
LLM可以快速合成常见的基因功能,作为研究人员的宝贵助手.
需要进一步开发和验证,但LLM提供了一个强大的新方法来补充现有的功能丰富方法.

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