基因表达钟:一种无监督的深度学习方法,用于从整个基因组表达中预测昼夜节律
Aram Ansary Ogholbake1, Qiang Cheng1
1Department of Computer Science and Department of Internal Medicine, Institute for Biomedical Informatics (IBI), University of Kentucky, Lexington, KY, USA.
Neural computing & applications
|August 13, 2025
概括
我们开发了一个深度神经网络,可以从基因表达数据中预测昼夜节律阶段,而无需时间标签. 这种方法可以准确地识别循环基因,并有助于理解像阿尔茨海默氏症这样的疾病.
科学领域:
- 时间生物学 时间生物学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 循环节律由分子钟调节,控制生理和行为过程.
- 昼夜节律的干扰与各种健康问题有关,需要研究它们.
- 对昼夜节律的基因表达数据的分析是具有挑战性的,因为缺乏时间标记样本.
研究的目的:
- 提出一种新的深度神经网络 (DNN) 方法,用于从未定时的基因表达数据中预测样本阶段.
- 解决在昼夜节律研究中识别循环基因和处理小数据集的挑战.
- 为了使人们能够研究昼夜节律而不依赖预先识别的昼夜基因.
主要方法:
- 使用最小扭曲嵌入框架进行初始候选循环基因选.
- 采用DNN的贪层级预训练来初始化隐藏的层面,并从有限的样本中捕获基因特征.
- 微调预先训练的DNN,以准确预测样本阶段.
主要成果:
- 在动物和人类数据集中证明了对样本阶段和循环基因的准确和强大的预测.
- 在没有预先了解保存的昼夜基因的情况下,成功识别了循环基因.
- 验证了阿尔茨海默病 (AD) 数据集的方法,确定了阿尔茨海默病患者中破坏的振荡基因.
结论:
- 提出的DNN方法有效地预测昼夜阶段,并从未定时的基因表达数据中识别周期性基因.
- 这种方法为研究昼夜生物学的研究提供了有价值的工具,尤其是在有限的样本大小的情况下.
- 在AD数据集中的发现突出了与神经退行性疾病中昼夜节律扰乱相关的潜在生物标志物和治疗目标.
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