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geneEX:一种综合的表型驱动算法,用于快速识别单一性疾病中的因果变异

Junyu Zhang^1,2, Dongyun Liu^3,4, Mei Chen⁵

¹Reproductive Medicine Center, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.

Molecular genetics & genomic medicine

|September 22, 2025

概括

这项研究介绍了geneEX,一种使用大型语言模型的算法,通过分析临床表型来提高罕见遗传疾病中识别病原变异的准确性和效率. geneEX自动化了变异优先级,有助于诊断单一性疾病.

科学领域:

遗传学是一个遗传学.
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

准确识别致病变体对于诊断单基因遗传疾病至关重要.
下一代测序 (NGS) 已经提高了诊断效率,但由于数据的复杂性,在确定致病变异方面仍然存在挑战.
当前的诊断方法在复杂的遗传数据分析的速度和准确性方面存在局限性.

研究的目的:

开发一种创新的表型驱动算法,geneEX,用于更好地识别罕见遗传疾病中的致病变体.
通过自动化数据分析和解释,提高诊断单一性疾病的效率和准确性.
利用大型语言模型技术进行精确的表型提取和基因关联.

主要方法:

开发了geneEX,这是一个以表型驱动的算法,集成大型语言模型技术.
实现了语义向量表示,用于自动获取人类表型本体 (HPO) 和与HPO相关的基因识别.
启用了患者自由文本表型和疾病表型之间的语义匹配,以改善病原性基因发现.
算法对候选致病变体进行排名,以便在罕见的遗传疾病中快速识别.

主要成果:

geneEX在虚拟和临床数据集上的病原体变异排名方面表现出强的表现.
自由文本表型的补充匹配显著提高了候选变体优先级的精度.
该算法有效地自动化了从临床样本到致病变种识别的过程.

结论:

geneEX实现了自动化HPO获取和致病变异的全过程识别.
整合自由文本的表型描述可以提高致病基因识别的准确性.
这种方法显著提高了在罕见遗传疾病中识别致病变异的精度和效率,支持单一的疾病诊断.

关键词:

由表型驱动的表型驱动优先级排名排名优先级排名罕见疾病的诊断罕见疾病的诊断

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