利用生成性人工智能来协助生物治疗罕见疾病的医疗行动
Enock Niyonkuru1,2,3, J Harry Caufield3, Leigh C Carmody2
1Trinity College, Hartford, CT 06106, United States.
Bioinformatics advances
|July 7, 2025
概括
AutoMAxO使用大型语言模型来自动化治疗罕见疾病的医疗行动. 该工具显著加快了注释过程,识别了958种罕见遗传疾病的新治疗注释.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 罕见疾病研究 罕见疾病研究
背景情况:
- 结构化的临床数据和本体学对于翻译研究至关重要.
- 医学行动本体学 (MAxO) 代表了临床管理行动.
- 对罕见疾病的MAxO手动生物治疗具有挑战性.
研究的目的:
- 开发一个半自动化工作流程,AutoMAxO,以简化MAxO生物处理.
- 利用大型语言模型 (LLM) 来有效地注释罕见疾病的医疗行动.
主要方法:
- 自动MAxO使用LLM从出版物摘要中提取候选治疗方法.
- 候选策划与MAxO,HPO和MONDO术语相匹配,使用LLM和后处理.
- 人类策展人审查并批准匹配的本体学术语.
主要成果:
- 应用了AutoMAxO工作流对37种罕见遗传疾病的摘要.
- 确定了958种新疗法注释,并添加到MAxO数据集中.
- 这种半自动化方法显著提高了生物治疗效率.
结论:
- AutoMAxO提供了一个可扩展的解决方案,用于在罕见疾病中注释医疗行动.
- 整合LLMs加速了罕见疾病治疗的发现和治愈.
- 作为一个Python包,AutoMAxO可用于更广泛的研究用途.
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