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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.
Gene regulation network reconstruction is the foundation of functional genomics research, which helps to understand the regulation mechanism between genes and explore the complex life system and its essence.According to the high computational complexity of traditional Bayesian methods, only a small-scale gene regulatory network can be constructed, However, there are many false positives in informatics and can not speculate on gene causal orientation.In this paper, we propose an OCMIPN algorithm for rapid construction of gene regulatory networks based on ordered conditional mutual information and finite parent nodes.The OCMIPN method first uses ordered conditions Then, according to the prior knowledge of gene regulation network topology, the number of parent nodes of each gene node is limited, and the genetic control network structure is deduced by Bayesian method, which can effectively reduce the time complexity of the algorithm. Experimental results on synthetic networks and real biomolecular networks show that the OCMIPN method can not only construct a high-precision gene regulation network with low time complexity, but also has better performance than LASSO, ARACNE, Scan BMA and LBN Popular algorithm.