在人类表型项目中,健康疾病连续性的深度表型化
Lee Reicher1,2,3,4, Smadar Shilo1,2,4,5, Anastasia Godneva1,2
1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Nature medicine
|July 15, 2025
概括
人类表型项目使用深度表型和人工智能来发现用于疾病预测的分子特征. 这种方法推进了个性化医疗和生物标志物发现,以获得更好的健康结果.
科学领域:
- 人类表现型定制 人类表现型定制
- 计算生物学 计算生物学
- 个性化医疗是个性化的医疗.
背景情况:
- 人类表型项目 (HPP) 是一项大规模的前性队列研究.
- 它旨在识别用于疾病诊断,预后和治疗的新型分子签名.
- 该项目还专注于开发基于人工智能 (AI) 的疾病发病和进展的预测模型.
研究的目的:
- 建立一个深度表型的队列,以推进生物标志物发现.
- 开发针对个性化医疗的多模式人工智能模型.
- 识别疾病的新型分子特征和预测模型.
主要方法:
- 参与者的纵向分析包括病史,生活方式,人体测量,血液测试,持续监测 (葡萄糖,睡眠),成像和多omics数据 (遗传学,转录学,微生物组,代谢学,免疫分析).
- 分析跨年龄和种族的表型变异.
- 开发和应用一个多模式基础AI模型,使用饮食和持续血糖监测数据的自我监督学习.
主要成果:
- 在HPP队列已经注册了大约28000名参与者,超过13000人完成了他们的初次访问.
- 分析揭示了基于年龄和种族的表型变异.
- 与现有方法相比,一种多式人工智能模型在预测疾病发病方面表现出优异的性能.
结论:
- 人类表型项目为生物标志物发现和开发人工智能驱动的个性化医学提供了一个强大的平台.
- 该研究强调了整合多模式数据的潜力,以了解疾病并改善患者的治疗结果.
- 开发的人工智能框架可以扩展到包含个性化数字双胞胎应用程序的额外数据模式.
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