中文摘要：

基因调控网络重建是功能基因组研究的基础,有助于理解基因间的调控机理,探索复杂的生命系统及其本质.针对传统贝叶斯方法计算复杂度高、仅能构建小规模基因调控网络,而信息论方法假阳性边较多、且不能推测基因因果定向问题.本文基于有序条件互信息和有限父结点,提出一种快速构建基因调控网络的OCMIPN算法.OCMIPN方法首先采用有序条件互信息构建基因调控相关网络;然后根据基因调控网络拓扑先验知识,限制每个基因结点的父结点数量,利用贝叶斯方法推断出基因调控网络结构,有效降低算法的时间计算复杂度.人工合成网络及真实生物分子网络上仿真实验结果表明：OCMIPN方法不仅能构建出高精度的基因调控网络,且时间计算复杂度较低,其性能优于LASSO、ARACNE、Scan BMA和LBN等现有流行算法.

英文摘要：

Inferring the gene regulatory networks （GRNs） structure is the research basis of functional genomics. GRNs can help to understand the regulatory mechanism among genes, exploring the essence of complex life system. Traditional Bayesian network 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. By using the ordered conditional mutual information （CMI） and limited parent node genes, in this work, we present a novel algorithm （namely OCMIPN） to fast infer GRNs from gene expression data. OCMIPN first uses ordered conditional mutual information to construct an initial GRN relation network. Then, according to the priori knowledge of gene regulatory network topology structure, BN method is employed to generate final GRNs by limiting the number of parent nodes for each gene, which significantly reduces the computational complexity. Tested on the synthetic networks as well as real biological molecular networks with different sizes and topologies, the results show that OCMIPN can infer RGNs with higher accuracy and low computational times. The OCMIPN＇s performance outperforms other state-of-the-art methods, such as LASSO, ARACNE, ScanBMA and LBN.