Meta-Analysis on Gene Regulatory Networks Discovered by Pairwise Granger Causality

摘要：

　　Identifying regulatory genes partaking in disease development is important to medical advances.Since gene expression data of multiple experiments exist,combining results from multiple gene regulatory network discoveries offers higher sensitivity and specificity.However,data for multiple experiments on the same problem may not possess the same set of genes,and hence many existing combining methods are not applicable.In this paper,we approach this problem using a number of meta-analysis methods and compare their performances.Simulation results show that vote counting is outperformed by methods belonging to the Fisher’s chi-square (FCS) family,of which FCS test is the best.Applying FCS test to the real human HeLa cell-cycle dataset,degree distributions of the combined network is obtained and compared with previous works.Consulting the BioGRID database reveals the biological relevance of gene regulatory networks discovered using the proposed method.

关键词： gene regulatory network meta-analysis multiple experiments pairwise Granger causality Fisher’s chi-square test

作者: Gary Hak Fui Tam Yeung Sam Hung Chunqi Chang

作者单位: Department of Electrical and Electronic Engineering The University of Hong KongHong Kong, China School of Electronic and Information EngineeringSoochow UniversitySuzhou, Jiangsu Province, China

会议类型: 国际会议

会议名称: 7th International Conference on Systems Biology(第7届计算系统生物学国际研讨会)(ISB2013)

会议地点: 安徽黄山

会议语种:英文

页码: 123-128

在线出版日期: 2013-08-22（万方平台首次上网日期，不代表论文的发表时间）