Inferring gene regulatory networks with a scale-free property based informative prior

Bo Yang, Jiangtao Xu, Bailin Liu, Zheng Wu
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引用次数: 4

Abstract

Constructing gene regulatory networks (GRNs) with microarray gene data is an essential and challenging task, especially when the underlying structures of networks are not observable in an experimental context. The paper proposes a boosting regression algorithm, called informative prior based GRN construction (ipGRN), to perform GRN inference. The ipGRN utilizes a scale-free based informative prior as well as Bayesian criterion measure to improve inference accuracy. In comparison with three existing methods (NIMOO, lasso and NIR), the ipGRN exhibits a significant improvement of computational accuracy and effectiveness on experiments of synthetic and real datasets. Furthermore, the method was applied to breast cancer data to reconstruct a sub-network of cancer susceptibility genes and achieved better inference results in detecting cancer associated genes.
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基于信息先验的无标度特性推断基因调控网络
利用微阵列基因数据构建基因调控网络(grn)是一项必要且具有挑战性的任务,特别是当网络的底层结构在实验环境中无法观察到时。本文提出了一种基于信息先验的GRN构造增强回归算法(ipGRN)来进行GRN推理。ipGRN利用基于无标度的信息先验和贝叶斯准则度量来提高推理精度。与NIMOO、lasso和NIR三种现有方法相比,ipGRN在合成数据集和真实数据集上的计算精度和效率都有显著提高。此外,将该方法应用于乳腺癌数据重构癌症易感基因子网络,在检测癌症相关基因方面取得了较好的推断结果。
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