基于部分互信息和贝叶斯打分函数的基因调控网络构建算法  

Inferring Gene Regulatory Networks Based on Part Mutual Information and Bayesian Scoring Function

作　　者：刘飞[1,2] 张绍武 高红艳[2] 

机构地区：[1]西北工业大学自动化学院信息融合教育部重点实验室,陕西西安710072 [2]宝鸡文理学院物理与光电技术学院,陕西宝鸡721016

出　　处：《西北工业大学学报》2017年第5期876-883,共8页Journal of Northwestern Polytechnical University

基　　金：国家自然科学基金(91430111;61473232;61170134)资助

摘　　要：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、PCalg、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- pies. 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.

关 键 词：部分互信息 互信息测试打分 贝叶斯网络 协方差矩阵 基因调控网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]