一种基于特征向量中心性的基因调控网络结构推测算法

An Algorithm for Gene Regulatory Network Structure Inference Based on Eigenvector Centrality

提出了一种基于特征向量中心性推断基因调控网络结构的算法,通过特征向量中心性挖掘基因在网络中的拓扑信息,结合基因对之间的相关性和拓扑信息构建完整的基因调控网络.算法在n个变量和n个样本的DREAM数据集以及包含9个变量和9个样本的大肠杆菌数据集上进行仿真测试,并与现有的基于距离相关性和网络拓扑中性的3种最先进的网络推理算法进行了比较,算法结果显示该方法能够提高基因调控网络结构的预测精度.

An algorithm is proposed to infer the structure of gene regulatory networks based on eigenvector centrality,which mines the topological information of genes in the network by eigenvector centrality and constructs a complete gene regulatory network by combining the correlation between gene pairs and topological information.The algorithm is simulated and tested on a DREAM dataset with n variables and n samples and an E.coli dataset containing 9 variables and 9 samples,and compared with three existing state-of-the-art network inference algorithms based on distance correlation and network topology neutrality,and the algorithm results show that the method can improve the prediction accuracy of the gene regulatory network structure.