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.传统的相关性系数模型、偏相关系数模型仅能发现基因间线性关系,而互信息和条件互信息可用于发现基因间的非线性关系,且能够处理高维低样本基因表达数据.但互信息过高估计基因间的相关性,条件互信息过低估计基因间的相关性,从而导致推断出的基因网络假阳性率和假阴性率较高,且不能推断基因调控方向.因而,基于部分互信息和贝叶斯打分函数,提出一种新的基因调控网络构建算法(命名为PMIBSF).基于部分互信息,PMIBSF算法首先删除初始基因相关网络中的冗余关联边,然后采用贝叶斯网络互信息测试打分函数学习贝叶斯网络结构,快速构建基因调控网络.在计算机模拟网络和真实生物分子网络上,仿真实验结果表明:PMIBSF性能优于目前较流行的LP、PC-alg、NARROMI和ARACNE算法,可高精度构建基因调控网络.%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 .The most widely used criteria are the Pearson correlation coefficient and partial correlation , but they can only measure linearly direct association and miss nonlinear associations .Mutual information ( MI) and conditional Mutual information ( CMI) not only can over-come those disadvantages , but also can process the gene expression data which are high dimensional and low sam -ples.MI and CMI are widely used in quantifying both linear and nonlinear associations , but they suffer from the se-rious problems of overestimation and underestimation .GRNS based on MI and CMI suffer from higher false-positive and false-negative problem and can ' t identify the directions of regulatory interactions .By using the partial mutual information (PMI) and Bayesian scoring function (BSF), in this work, we present a novel algorithm (namely PMIBSF ) .Tested on the Synthetic networks as well as real biological molecular networks with different sizes and to -pologies, the results show that PMIBSF can infer RGNs with higher accuracy .The PMIBSF's performance outper-forms other state-of-the-art methods, such as LP , PC-alg, NARROMI and ARACNE.
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