DeepCE:一个深度学习框架,用于在单细胞RNA测序数据中进行相关性增强的基因调控网络推断.
Qianqian Wu1, Xingmiao Dai1, Shiyi Lou1
1School of Mathematics, Hefei University of Technology, Hefei, Anhui 230009, China.
Bioinformatics advances
|February 20, 2026
概括
我们开发了DeepCE,这是一个深度学习框架,用于推断基因调节网络 (GRNs). DeepCE提高了理解基因表达动态和细胞异质性的准确性和可靠性.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 单细胞RNA测序 (scRNA-seq) 揭示了基因表达动态和细胞异质性.
- 深度学习 (DL) 在推断基因调节方面表现有前途,但与复杂的机制作斗争.
- 需要新的算法来提高基因调节网络 (GRN) 推断的有效性和可靠性.
研究的目的:
- 引入DeepCE,这是一个新的DL框架,旨在用于相关性增强的GRN推理.
- 通过整合先进的DL技术来提高GRN推断的准确性和稳定性.
主要方法:
- DeepCE 集成了双向封闭循环单元 (GRU) 与卷积神经网络 (CNN).
- 双向GRU捕获基因表达数据中的动态时间依赖.
- 在scRNA-seq数据中,CNN分析了局部空间模式,以发现复杂的基因相互作用.
主要成果:
- DeepCE 增强了动态基因调节的提取.
- 该框架平滑杂的基因表达数据,提取时间滞后的调节信号,并过虚假的相关性.
- 对老鼠和人类数据集的实验表明,DeepCE的表现优于现有方法,获得了优异的AUROC和AUPR分数.
结论:
- DeepCE为高质量的GRN推理提供了一个强大而可靠的框架.
- 提出的方法有助于从单细胞数据了解基因调节机制.
- 与当前最先进的方法相比,DeepCE提供了更好的准确性和稳定性.
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