将电子健康记录连接到生物医学知识图表,以链接 atop 皮肤炎中的临床表型和分子内型
Francesca Frau1, Paul Loustalot2, Margaux Törnqvist2
1Sanofi R&D, Development Real World Evidence, 65926, Frankfurt am Main, Germany.
Scientific reports
|January 24, 2025
概括
这项研究将现实世界的数据与生物医学知识图表结合起来,使用图表机器学习来推进精准医学. 该方法确定了亚托皮性皮肤炎的七个不同的患者亚组,为个性化治疗铺平了道路.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 精准医学需要整合多样化的数据 (基因,环境,生活方式) 以进行量身定制的治疗.
- 生物医学知识图 (BKG) 传统上缺乏现实世界数据 (RWD),限制了患者层面的洞察力.
- 整合RWD与BKG对于理解复杂疾病和实现个性化医疗至关重要.
研究的目的:
- 通过图形机器学习 (ML) 提出和验证用于整合RWD和BKG的新方法.
- 开发一个患者代表模型,以促进ML发现的解释和扩展.
- 通过综合数据分析,识别复杂疾病的亚型及其病理生物学机制.
主要方法:
- 建立了真实世界数据 (RWD) 和生物医学知识图 (BKG) 之间的数据链接.
- 开发了一种新的患者表征,使用图形ML应用于BKG.
- 将该方法应用于亚托邦性皮炎,以根据临床和基因组数据识别患者子组.
主要成果:
- 成功将RWD与BKG联系起来,从而实现更全面的数据集成.
- 图形ML方法促进了发现的解释和扩展,特别是用于疾病亚型的识别.
- 在阿托皮性皮肤炎中确定了七个不同的患者亚组,每个都有独特的临床和基因组特征.
结论:
- 拟议的方法是迈向完全整合的精准医学战略的重要一步.
- 通过图ML将RWD与BKG集成,可以提高复杂疾病亚型及其机制的识别.
- 这种方法有望在各种复杂的疾病中推进个性化治疗策略.
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