A new multiple regression approach for the construction of genetic regulatory networks
A new multiple regression approach for the construction of genetic regulatory networks
复制标题
构建遗传调控网络的新多元回归方法
DOI：
10.1016/j.artmed.2009.11.001
复制
发表时间：
2010-02-01
影响因子：
7.5
通讯作者：
Guo, Dianjing
中科院分区：
文献类型：
--
作者：
Zhang, Shu-Qin;Ching, Wai-Ki;Guo, Dianjing
目的：根据给定的时间序列基因表达数据重建基因调控网络是系统生物学中的一个重要研究课题。构建基因调控网络的主要困难之一在于，实际数据集具有大量的基因而不是少量的采样时间点。在本文中，我们提出了一个新的线性回归模型，可以克服这一困难，揭示遗传网络中的调控关系。方法：所提出的多元回归模型利用了真实生物网络的无标度特性。特别是，利用这种无标度性质和适当的统计检验构造了一个过滤器，以消除基因之间的冗余交互作用。结果：基于酵母基因表达数据的数值算例表明，该模型与实际数据吻合较好。结论：基于真实生物网络的无标度特性，提出了一种新的用于基因调控网络推理的多元回归模型。利用酵母细胞周期基因表达数据集的数值结果表明了该方法的有效性。我们期望所提出的方法可以广泛应用于利用来自不同物种的高通量基因表达数据进行遗传网络推断，以用于系统生物学发现。(C)2009爱思唯尔B.V.保留所有权利。
Objective: Re-construction of a genetic regulatory network from a given time-series gene expression data is an important research topic in systems biology. One of the main difficulties in building a genetic regulatory network lies in the fact that practical data set has a huge number of genes vs. a small number of sampling time points. In this paper, we propose a new linear regression model that may overcome this difficulty for uncovering the regulatory relationship in a genetic network.Methods: The proposed multiple regression model makes use of the scale-free property of a real biological network. In particular, a filter is constructed by using this scale-free property and some appropriate statistical tests to remove redundant interactions among the genes. A model is then constructed by minimizing the gap between the observed and the predicted data.Results: Numerical examples based on yeast gene expression data are given to demonstrate that the proposed model fits the practical data very well. Some interesting properties of the genes and the underlying network are also observed.Conclusions: In conclusion, we propose a new multiple regression model based on the scale-free property of real biological network for genetic regulatory network inference. Numerical results using yeast cell cycle gene expression dataset show the effectiveness of our method. We expect that the proposed method can be widely used for genetic network inference using high-throughput gene expression data from various species for systems biology discovery. (C) 2009 Elsevier B.V. All rights reserved.