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在未被诊断的队列中自动共享表型发现,用于罕见疾病研究

Aaron J Masino¹, Ranga Baminiwatte¹

¹School of Computing, Clemson University, Clemson, SC, U.S.A.

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications

|April 7, 2025

概括

由于未知的基因与表型联系,诊断罕见疾病是很困难的. 这项研究引入了一种使用自然语言处理 (NLP) 的自动化方法,以有效地识别共享的患者表型,帮助基因发现.

科学领域:

遗传学遗传学是一个
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:

罕见疾病的诊断受到基因与表型关联的不完全知识的阻碍.
目前的基因对患者研究通常依赖于手动表型审查,这耗时且不可扩展.

研究的目的:

开发和评估一种新的自动化方法,用于识别罕见疾病队列中共享的表型.
提高基因与表型关联发现的效率和可扩展性,使用基因与患者的范式.

主要方法:

使用遗传搜索和比较表型术语嵌入的健身功能开发了一种自动化表型识别方法.
先进的自然语言处理 (NLP) 模型被用来从表型术语描述中生成嵌入.
在5,076种门德尔病的模拟患者队列中,使用人类现象型本体学资源创建了一个模拟患者队列.

主要成果:

自动化方法成功地确定了与大多数模拟疾病表型密切匹配的表型术语.
该方法在不同的注释不准确性和噪音条件下表现出稳健性.
该方法可以实现可扩展的基因对患者研究,用于发现罕见疾病的关联.

结论:

拟议的自动化方法显著增强了罕见疾病研究的基因对患者范式.
这种方法可以加速发现基因与表型的关联,提高诊断能力.
基于NLP的技术为罕见疾病遗传学的关键挑战提供了一个可扩展的解决方案.

关键词:

在NLP中,我们使用了NLP.生物医学信息学生物医学信息学遗传算法遗传算法在本体论上,本体论是存在的.现象类型现象类型

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