从单细胞RNA测序数据推断基因调控网络,通过双重角色图进行对比学习.
Qiyuan Guan1, Jiating Yu2, Jieyi Pan1
1School of Mathematics, Shandong University, Jinan, 250100, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|November 29, 2025
概括
RegGAIN是一种新的深度学习模型,可以从单细胞数据中准确推断基因调节网络 (GRNs). 这种方法通过改进GRN重建来增强对细胞过程和疾病机制的理解.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 基因调控网络 (GRN) 的推断对于理解细胞机制至关重要.
- 由于噪音和稀疏性,现有的方法难以处理单细胞RNA测序数据.
- 需要精确的细胞类型特定的GRNs来深入了解细胞身份和疾病.
研究的目的:
- 介绍RegGAIN,一种新的深度学习模型,用于从单细胞转录基因数据推断基因调节网络.
- 为了提高GRN重建的准确性和稳定性.
- 为了使特定条件和动态监管程序的发现.
主要方法:
- RegGAIN利用自我监督的对比学习来增强基因嵌入.
- 双重角色表示是使用单独的编码器学习的,用于定向和不同的模式.
- 模型性能与现有的GRN推理方法进行评估.
主要成果:
- 在GRN重建准确性和稳定性方面,RegGAIN始终优于当前的方法.
- 预测的调节相互作用使用外部表观遗传数据进行验证.
- 该模型成功地识别了GRN重新布线和动态监管程序.
结论:
- RegGAIN为基因调节网络推断提供了一个强大的和可泛化的框架.
- 该模型提供了对细胞调节在各种生物环境中的更深入的见解.
- RegGAIN推进了计算生物学领域的转录基因数据分析.
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