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大型语言模型的应用,以表型为基础,对罕见疾病患者的致病基因进行优先排序

Şenay Kafkas^1,2, Marwa Abdelhakim^3,4, Azza Althagafi^3,5

¹Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 23955, Thuwal, Saudi Arabia. senay.kafkas@kaust.edu.sa.

Scientific reports

|April 29, 2025

概括

大型语言模型 (LLM) 在识别罕见遗传疾病的基因疾病联系方面表现有前途. 这些人工智能模型可以像传统生物信息学工具一样有效地或比传统生物信息学工具更好地优先考虑候选基因,帮助诊断.

科学领域:

基因组学和生物信息学
人工智能在医学中的应用
罕见疾病的诊断罕见疾病的诊断

背景情况:

基因疾病关联的传统计算方法依赖于基因组和表型数据,通常受到数据质量和注释完整性的限制.
现有的基于表型的方法将患者表型与基因型-表型数据库进行比较,使用语义相似性,面临来自资源质量的限制.
在广泛的文本和数据上训练的大型语言模型 (LLM) 提供了在各种科学领域进行复杂的问答的潜力.

研究的目的:

评估大型语言模型 (LLM) 在与已建立的生物信息学方法相比优先考虑与疾病相关的基因方面的有效性.
评估LLM在为未被诊断的罕见病患者提供诊断支持方面的实用性.
探索LLMs作为传统生物信息学工具的潜在替代品,用于遗传疾病中的基因优先级.

主要方法:

对LLM性能与现有的生物信息学工具进行比较分析,以优先考虑与疾病相关的基因.
将LLM方法应用于患有罕见疾病的未被诊断的患者队列.
评估LLMs识别诊断支持可信候选基因的能力.

主要成果:

从5到100个基因的候选组中,LLM在优先考虑与疾病相关的基因方面表现出与专用生物信息学方法相比或更好的表现.
该LLM方法成功为未被诊断的罕见病患者提供诊断支持,识别了可信的候选基因.
根据表型信息,LLM有效地优先考虑与疾病相关的基因,反映或超越传统方法.

结论:

在传统的生物信息学方法中,LLM提供了一个可行的替代方案,可以使用表型数据来优先考虑与疾病相关的基因.
临床医学具有提高诊断准确度和简化罕见遗传疾病诊断过程的潜力.
这些发现表明,LLM可以在临床环境中作为一种有价值的工具来诊断罕见疾病.

关键词:

诊断支持诊断支持基因优先级的排序大型语言模型.现象类型现象类型罕见疾病是一种罕见的疾病.

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