PROTECT:使用无监督学习来预测蛋白质昼夜时间
Aram Ansary Ogholbake1, Qiang Cheng1
1Department of Internal Medicine and Department of Computer Science, University of Kentucky, Lexington, KY, USA.
iScience
|October 23, 2025
概括
循环节律扰乱与阿尔茨海默病 (AD) 有关. 一种新的深度学习方法PROTECT分析了无时间标签的蛋白质组数据,以揭示阿尔茨海默病患者的显著昼夜变化.
科学领域:
- 时间生物学 时间生物学
- 神经科学是一个神经科学.
- 蛋白质组学是指蛋白质组学.
- 人工智能的人工智能
背景情况:
- 循环节律支配着人类生理学,它们的干扰与阿尔茨海默氏症 (AD) 等疾病有关.
- 蛋白质组数据集往往缺乏关键的时间标签信息,对昼夜节律分析提出重大挑战 (样本小,高维度,噪声).
研究的目的:
- 介绍PROTECT,一种无监督的深度学习方法,用于从未标记的蛋白质组数据中预测昼夜样本阶段.
- 应用PROTECT来识别阿尔茨海默病 (AD) 中的昼夜干扰,使用死后的人类大脑和尿液蛋白质组数据.
- 为了比较AD和对照对象之间的昼夜模式,以了解AD相关的昼夜失调.
主要方法:
- 开发了PROTECT,这是一个无监督的深度学习算法,利用贪的层级智能预训练和基于共数的微调.
- 在现有的时间标记蛋白质组数据集上验证了PROTECT的准确性.
- 应用PROTECT到从死后大脑区域和尿液样本中未标记的人类蛋白质组数据.
主要成果:
- 在不需要时间标签或对节律蛋白质的先验知识的情况下,PROTECT准确地预测昼夜样本阶段.
- 对AD蛋白质组数据的分析揭示了显著的昼夜干扰,识别了具有保留,丢失或改变节律性的蛋白质.
- 维持节律性的蛋白质表现出特定区域的相位移和振幅变化;丰富分析提供了对改变节律性的功能洞察.
结论:
- PROTECT是一种有效的工具,用于对未标记的蛋白质组数据进行昼夜分析,克服了常见的数据集限制.
- 这项研究提供了系统的证据,证明阿尔茨海默氏症 (AD) 大脑和尿液蛋白质数据中昼夜模式的变化.
- 这些发现突出了关键蛋白质和功能途径,涉及与AD相关的昼夜失调,提供了潜在的治疗点.
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