基因调节网络推断使用混合规范规范化的多变量模型与共变性选择
Alain J Mbebi1,2, Zoran Nikoloski1,2
1Bioinformatics Department, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Germany.
PLoS computational biology
|July 31, 2023
概括
重建基因调节网络 (GRNs) 是一个挑战. 联合建模多个目标基因可以改善GRN推断,为系统生物学应用提供了强大的替代方案.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 从转录组学数据中重建基因调节网络 (GRNs) 是一个重大挑战.
- 非线性方法改善了GRN重建,但在线性假设下联合建模多个目标基因的好处尚不清楚.
研究的目的:
- 通过共同建模多个目标基因来开发和评估GRN重建的新方法.
- 评估同步建模是否与现有方法相比提高了GRN推理准确度.
主要方法:
- 提出了两种新的方法,将规范化多变量回归和图形模型混合在一起.
- 利用L2,1-规范与GRN重建的经典规范化技术.
- 通过使用DREAM5挑战数据和来自大肠杆菌和大肠杆菌的数据集来验证模型.
主要成果:
- 提出的模型在各种数据集中始终表现出良好的表现.
- 通过同时对多个目标基因进行建模,提高了GRN推断准确度.
- 成功确定了与实验证据相一致的主调节剂在大肠杆菌中的实验证据.
结论:
- 同时对多个目标基因进行建模显著改善了GRN推断.
- 开发的方法为系统生物学中GRN重建提供了可靠的替代方案.
- 该方法可方便对主调节器可塑性的准确预测和分析.
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