从多个omics数据中量化个人健康状况,按健康状况多重表现.
Xinyan Zhang1, Chengming Zhang2, Yanpu Wu1
1Key Laboratory of Systems Biology, Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024 China.
Phenomics (Cham, Switzerland)
|February 9, 2026
概括
使用多omics数据量化个体健康状况对于预防疾病至关重要. 新的Health State Manifold (HSM) 方法准确地测量健康状况和恢复力,帮助个性化和预防性医学.
科学领域:
- 综合生物学和系统医学 综合生物学和系统医学
- 计算生物学和生物信息学
背景情况:
- 准确的量化个体健康状况从多omics数据对于疾病预防和干预至关重要.
- 当前的方法往往侧重于分类,缺乏动态系统的视角.
- 个人健康可以被视为一个非平衡的稳定状态,具有平衡和表型潜力.
研究的目的:
- 开发一种新的方法,使用多个OMIC数据量化个体健康状况.
- 通过同源静态和表型潜力来描述健康状况.
- 为了实现个性化和预防性医学的强大和准确的健康状况量化.
主要方法:
- 提出了健康状态多重 (HSM) 方法.
- 从动态网络生物标志物和扩散图理论中衍生出HSM.
- 将HSM应用于多omics数据以量化健康状况.
主要成果:
- 通过HSM方法,成功量化了个人的健康状况.
- 在糖尿病 (老鼠) 和Roux-en-Y胃绕道 (人类) 研究中,在描述疾病进展和恢复方面表现出有效性.
- 验证了HSM方法的稳定性和准确性.
结论:
- 医疗健康分析 (HSM) 方法为了解和量化个体健康动态提供了一个强大的工具.
- 提供了推进个性化医疗和预防策略的潜力.
- 强调动态系统方法在健康和疾病研究中的实用性.
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