整合可解释的人工智能与多态系统生物学和电子健康记录数据挖掘,以实现个性化药物重定向在阿尔茨海默病中
Mohammadsadeq Mottaqi1, Pengyue Zhang2, Lei Xie1,3,4,5
1Ph.D. Program in Biochemistry, The Graduate Center, The City University of New York, 365 Fifth Avenue, New York, NY 10016, United States.
Briefings in bioinformatics
|November 28, 2025
概括
我们开发了PRISM-ML,这是一个人工智能管道,分析多组数据,以发现阿尔茨海默病 (AD) 的分子亚型,并确定潜在的药物重定向候选人. 在现实数据中,prometazine显示AD发病率降低,支持其治疗潜力.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 遗传学 遗传学 是一个
背景情况:
- 阿尔茨海默病 (AD) 呈现出显著的分子异质性,使治疗策略复杂化.
- 了解患者特异性的分子差异对于开发有效疗法至关重要.
研究的目的:
- 引入PRISM-ML,一种可解释的机器学习管道,用于分析AD中的多态数据.
- 确定患者特异性的生物标志物,并预测AD的药物重用机会.
主要方法:
- 来自2105个大脑样本 (AD患者和对照组) 的综合转录和基因组数据.
- 利用可解释的机器学习 (随机森林,SHAP) 来发现生物标志物.
- 应用网络分析和知识图表用于药物重用预测.
主要成果:
- 确定了36个分子上不同的AD亚组织和262个关键调节基因.
- 预测六种FDA批准的药物用于重新定位,包括promethazine.
- 现实世界的数据显示,普罗梅他与明显较低的AD发病率相关 (57%-62%的减少).
结论:
- PRISM-ML有效地阐明了AD的分子多样性,并确定了药物重定向候选人.
- 有希望的现实世界的证据支持以promethazine为AD预防或治疗的重用.
- PRISM-ML管道可适应其他复杂疾病,推进精密医学.
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