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基因调控网络稀疏逼近的启发式方法。

Heuristic approach to sparse approximation of gene regulatory networks.

作者信息

Andrecut M, Huang S, Kauffman S A

机构信息

Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta, Canada.

出版信息

J Comput Biol. 2008 Nov;15(9):1173-86. doi: 10.1089/cmb.2008.0087.

Abstract

Determining the structure of the gene regulatory network using the information in genomewide profiles of mRNA abundance, such as microarray data, poses several challenges. Typically, "static" rather than dynamical profile measurements, such as those taken from steady state tissues in various conditions, are the starting point. This makes the inference of causal relationships between genes difficult. Moreover, the paucity of samples relative to the gene number leads to problems such as overfitting and underconstrained regression analysis. Here we present a novel method for the sparse approximation of gene regulatory networks that addresses these issues. It is formulated as a sparse combinatorial optimization problem which has a globally optimal solution in terms of l(0) norm error. In order to seek an approximate solution of the l(0) optimization problem, we consider a heuristic approach based on iterative greedy algorithms. We apply our method to a set of gene expression profiles comprising of 24,102 genes measured over 79 human tissues. The inferred network is a signed directed graph, hence predicts causal relationships. It exhibits typical characteristics of regulatory networks organism with partially known network topology, such as the average number of inputs per gene as well as the in-degree and out-degree distribution.

摘要

利用全基因组mRNA丰度图谱(如微阵列数据)中的信息来确定基因调控网络的结构,面临着诸多挑战。通常,“静态”而非动态图谱测量,例如在各种条件下从稳态组织获取的数据,是研究的起点。这使得推断基因之间的因果关系变得困难。此外,相对于基因数量而言样本数量匮乏会导致诸如过拟合和回归分析约束不足等问题。在此,我们提出一种新方法,用于对基因调控网络进行稀疏逼近,以解决这些问题。该方法被表述为一个稀疏组合优化问题,就l(0)范数误差而言具有全局最优解。为了寻求l(0)优化问题的近似解,我们考虑基于迭代贪心算法的启发式方法。我们将我们的方法应用于一组包含在79个人类组织中测量的24,102个基因的基因表达图谱。推断出的网络是一个带符号的有向图,因此可以预测因果关系。它展现出具有部分已知网络拓扑结构的生物体调控网络的典型特征,例如每个基因的平均输入数量以及入度和出度分布。

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