Inference of Gene Regulatory Network Based on Local Bayesian Networks.
Inference of Gene Regulatory Network Based on Local Bayesian Networks.
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基于局部贝叶斯网络的基因调控网络推理
DOI：
10.1371/journal.pcbi.1005024
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发表时间：
2016-08
影响因子：
4.3
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Liu F;Zhang SW;Guo WF;Wei ZG;Chen L
从基因表达数据出发重构基因调控网络，可有效挖掘基因间调控关系，深层次地理解生物调控过程。在过去的几十年中，已经引入了许多计算方法来推断GRNs。然而，它们中的许多仍然遭受各种问题，贝叶斯网络（Bayesian Network，BN）方法计算复杂度高，无法处理大规模网络，而基于信息论的方法无法识别调控交互作用的方向，也存在假阳性/假阴性问题。为了克服这一局限性，本文提出了一种基于局部贝叶斯网络（LBN）的基因表达数据GRNs推断算法，该算法利用网络分解策略和假阳性边缘消除策略，从基因表达数据中推断GRNs。具体地说，LBN算法首先利用条件互信息（CMI）构造初始网络（GRN），然后将初始网络分解为若干个局部网络（GRNs）。在此基础上，利用BN方法，通过选择每个基因的k近邻作为候选调控基因，生成一系列局部BN，从而大大减少了从所有可能的GRN结构中进行指数搜索所需的空间。通过执行CMI，集成这些本地BN形成试验性网络或GRN，这减少了GRN中的冗余规则，并因此减轻了假阳性问题。最终网络或GRN可以通过在试验网络上迭代地执行CMI和局部BN来获得。在迭代过程中，虚假或冗余的规定被逐步剔除。在DREAM挑战的GRN数据集和大肠杆菌SOS DNA修复网络上的测试结果表明，LBN算法的准确性和鲁棒性明显优于ARACNE、GENIE 3和NARROMI算法。特别是，基于局部贝叶斯网络的分解策略不仅有效地减少了局部GRNN的计算量，而且还能识别规则的方向.
The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce the computational cost of BN due to much smaller sizes of local GRNs, but also identify the directions of the regulations.